Writing R ExtensionsVersion 3.0.2 (2013-09-25)

R Core Team

This manual is for R, version 3.0.2 (2013-09-25).

Copyright c© 1999–2013 R Core Team

Permission is granted to make and distribute verbatim copies of this manual providedthe copyright notice and this permission notice are preserved on all copies.

Permission is granted to copy and distribute modified versions of this manual underthe conditions for verbatim copying, provided that the entire resulting derived workis distributed under the terms of a permission notice identical to this one.

Permission is granted to copy and distribute translations of this manual into an-other language, under the above conditions for modified versions, except that thispermission notice may be stated in a translation approved by the R Core Team.

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Table of Contents

Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1 Creating R packages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.1 Package structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.1.1 The DESCRIPTION file . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41.1.2 Licensing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71.1.3 Package Dependencies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81.1.4 The INDEX file . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101.1.5 Package subdirectories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111.1.6 Data in packages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141.1.7 Non-R scripts in packages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

1.2 Configure and cleanup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151.2.1 Using Makevars . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

1.2.1.1 OpenMP support . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211.2.1.2 Using pthreads . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221.2.1.3 Compiling in sub-directories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

1.2.2 Configure example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231.2.3 Using F95 code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

1.3 Checking and building packages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 251.3.1 Checking packages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 261.3.2 Building package tarballs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 291.3.3 Building binary packages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

1.4 Writing package vignettes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 311.4.1 Encodings and vignettes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 331.4.2 Non-Sweave vignettes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

1.5 Package namespaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 341.5.1 Specifying imports and exports . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 351.5.2 Registering S3 methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 351.5.3 Load hooks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 361.5.4 useDynLib . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 361.5.5 An example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 381.5.6 Namespaces with S4 classes and methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

1.6 Writing portable packages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 401.6.1 PDF size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 431.6.2 Check timing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 431.6.3 Encoding issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 441.6.4 Binary distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

1.7 Diagnostic messages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 451.8 Internationalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

1.8.1 C-level messages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 461.8.2 R messages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 471.8.3 Preparing translations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

1.9 CITATION files . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 481.10 Package types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

1.10.1 Frontend . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 491.11 Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

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2 Writing R documentation files . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 502.1 Rd format . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

2.1.1 Documenting functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 512.1.2 Documenting data sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 552.1.3 Documenting S4 classes and methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 562.1.4 Documenting packages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

2.2 Sectioning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 572.3 Marking text . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 572.4 Lists and tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 592.5 Cross-references . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 602.6 Mathematics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 602.7 Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 612.8 Insertions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 612.9 Indices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 622.10 Platform-specific documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 622.11 Conditional text . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 632.12 Dynamic pages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 632.13 User-defined macros . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 642.14 Encoding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 642.15 Processing documentation files . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 652.16 Editing Rd files . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

3 Tidying and profiling R code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 673.1 Tidying R code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 673.2 Profiling R code for speed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 673.3 Profiling R code for memory use . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

3.3.1 Memory statistics from Rprof . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 693.3.2 Tracking memory allocations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 703.3.3 Tracing copies of an object . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

3.4 Profiling compiled code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 703.4.1 Linux . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

3.4.1.1 sprof . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 713.4.1.2 oprofile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

3.4.2 Solaris . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 723.4.3 OS X . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

4 Debugging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 734.1 Browsing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 734.2 Debugging R code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 744.3 Using gctorture and memory access . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78

4.3.1 Using gctorture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 784.3.2 Using valgrind . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 784.3.3 Using AddressSanitizer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 804.3.4 Fortran array bounds checking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80

4.4 Debugging compiled code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 814.4.1 Finding entry points in dynamically loaded code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 824.4.2 Inspecting R objects when debugging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82

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5 System and foreign language interfaces . . . . . . . . . . . . . . . . . . . . . 855.1 Operating system access . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 855.2 Interface functions .C and .Fortran . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 855.3 dyn.load and dyn.unload . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 875.4 Registering native routines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88

5.4.1 Speed considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 905.4.2 Linking to native routines in other packages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91

5.5 Creating shared objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 925.6 Interfacing C++ code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 935.7 Fortran I/O . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 945.8 Linking to other packages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95

5.8.1 Unix-alikes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 955.8.2 Windows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96

5.9 Handling R objects in C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 975.9.1 Handling the effects of garbage collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 985.9.2 Allocating storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 995.9.3 Details of R types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 995.9.4 Attributes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1005.9.5 Classes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1025.9.6 Handling lists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1025.9.7 Handling character data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1035.9.8 Finding and setting variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1035.9.9 Some convenience functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104

5.9.9.1 Semi-internal convenience functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1055.9.10 Named objects and copying . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105

5.10 Interface functions .Call and .External . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1065.10.1 Calling .Call . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1065.10.2 Calling .External . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1065.10.3 Missing and special values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108

5.11 Evaluating R expressions from C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1085.11.1 Zero-finding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1105.11.2 Calculating numerical derivatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111

5.12 Parsing R code from C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1145.12.1 Accessing source references . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115

5.13 External pointers and weak references . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1165.13.1 An example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117

5.14 Vector accessor functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1175.15 Character encoding issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118

6 The R API: entry points for C code . . . . . . . . . . . . . . . . . . . . . . . 1196.1 Memory allocation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119

6.1.1 Transient storage allocation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1196.1.2 User-controlled memory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120

6.2 Error handling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1216.2.1 Error handling from FORTRAN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121

6.3 Random number generation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1216.4 Missing and IEEE special values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1226.5 Printing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122

6.5.1 Printing from FORTRAN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1226.6 Calling C from FORTRAN and vice versa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1236.7 Numerical analysis subroutines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123

6.7.1 Distribution functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1246.7.2 Mathematical functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125

iv

6.7.3 Numerical Utilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1256.7.4 Mathematical constants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127

6.8 Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1276.9 Integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1286.10 Utility functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1296.11 Re-encoding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1316.12 Allowing interrupts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1316.13 Platform and version information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1326.14 Inlining C functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1326.15 Controlling visibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1326.16 Using these functions in your own C code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1336.17 Organization of header files . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134

7 Generic functions and methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1357.1 Adding new generics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136

8 Linking GUIs and other front-ends to R . . . . . . . . . . . . . . . . . . 1378.1 Embedding R under Unix-alikes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137

8.1.1 Compiling against the R library . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1398.1.2 Setting R callbacks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1398.1.3 Registering symbols . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1428.1.4 Meshing event loops . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1428.1.5 Threading issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143

8.2 Embedding R under Windows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1448.2.1 Using (D)COM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1448.2.2 Calling R.dll directly . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1448.2.3 Finding R HOME . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147

Function and variable index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148

Concept index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151

Acknowledgements 1

Acknowledgements

The contributions to early versions of this manual by Saikat DebRoy (who wrote the first draftof a guide to using .Call and .External) and Adrian Trapletti (who provided information onthe C++ interface) are gratefully acknowledged.

Chapter 1: Creating R packages 2

1 Creating R packages

Packages provide a mechanism for loading optional code, data and documentation as needed.The R distribution itself includes about 30 packages.

In the following, we assume that you know the library() command, including its lib.locargument, and we also assume basic knowledge of the R CMD INSTALL utility. Otherwise, pleaselook at R’s help pages on

?library

?INSTALL

before reading on.

For packages which contain code to be compiled, a computing environment including a num-ber of tools is assumed; the “R Installation and Administration” manual describes what isneeded for each OS.

Once a source package is created, it must be installed by the command R CMD INSTALL. SeeSection “Add-on-packages” in R Installation and Administration.

Other types of extensions are supported (but rare): See Section 1.10 [Package types], page 48.

Some notes on terminology complete this introduction. These will help with the reading ofthis manual, and also in describing concepts accurately when asking for help.

A package is a directory of files which extend R, a source package (the master files of apackage), or a tarball containing the files of a source package, or an installed package, the resultof running R CMD INSTALL on a source package. On some platforms (notably OS X and Windows)there are also binary packages, a zip file or tarball containing the files of an installed packagewhich can be unpacked rather than installing from sources.

A package is not1 a library. The latter is used in two senses in R documentation.

• A directory into which packages are installed, e.g. /usr/lib/R/library: in that sense it issometimes referred to as a library directory or library tree (since the library is a directorywhich contains packages as directories, which themselves contain directories).

• That used by the operating system, as a shared, dynamic or static library or (especially onWindows) a DLL, where the second L stands for ‘library’. Installed packages may containcompiled code in what is known on Unix-alikes as a shared object and on Windows as aDLL. The concept of a shared library (dynamic library on OS X) as a collection of compiledcode to which a package might link is also used, especially for R itself on some platforms.On most platforms these concepts are interchangeable (shared objects and DLLs can bothbe loaded into the R process and be linked against), but OS X distinguishes between sharedobjects (extension .so) and dynamic libraries (extension .dylib).

There are a number of well-defined operations on source packages.

• The most common is installation which takes a source package and installs it in a libraryusing R CMD INSTALL or install.packages.

• Source packages can be built. This involves taking a source directory and creating a tarballready for distribution, including cleaning it up and creating PDF documentation from anyvignettes it may contain. Source packages (and most often tarballs) can be checked, whena test installation is done and tested (including running its examples); also, the contents ofthe package are tested in various ways for consistency and portability.

• Compilation is not a correct term for a package. Installing a source package which containsC, C++ or Fortran code will involve compiling that code. There is also the possibility of‘byte’ compiling the R code in a package (using the facilities of package compiler): already

1 although this is a persistent mis-usage. It seems to stem from S, whose analogues of R’s packages were officiallyknown as library sections and later as chapters, but almost always referred to as libraries.

Chapter 1: Creating R packages 3

the base and recommended packages are byte-compiled. At some future time this might bedone routinely, so compiling a package may come to mean compiling its R code.

• It used to be unambiguous to talk about loading an installed package using library(),but since the advent of package namespaces this has been less clear: people now often talkabout loading the package’s namespace and then attaching the package so it becomes visibleon the search path. Function library performs both steps, but a package’s namespace canbe loaded without the package being attached (for example by calls like splines::ns).

The concept of lazy loading of code or data is mentioned at several points. This is part ofthe installation, always selected for R code but optional for data. When used the R objects ofthe package are created at installation time and stored in a database in the R directory of theinstalled package, being loaded into the session at first use. This makes the R session start upfaster and use less (virtual) memory. (For technical details, see Section “Lazy loading” in RInternals.)

CRAN is a network of WWW sites holding the R distributions and contributed code, especiallyR packages. Users of R are encouraged to join in the collaborative project and to submit theirown packages to CRAN: current instructions are linked from http://CRAN.R-project.org/

banner.shtml#submitting.

1.1 Package structure

The sources of an R package consists of a subdirectory containing a files DESCRIPTION andNAMESPACE, and the subdirectories R, data, demo, exec, inst, man, po, src, tests, tools andvignettes (some of which can be missing, but which should not be empty). The packagesubdirectory may also contain files INDEX, configure, cleanup, LICENSE, LICENCE and NEWS.Other files such as INSTALL (for non-standard installation instructions), README/README.md2, orChangeLog will be ignored by R, but may be useful to end users. The utility R CMD build mayadd files in a build directory (but this should not be used for other purposes).

Except where specifically mentioned,3 packages should not contain Unix-style ‘hidden’files/directories (that is, those whose name starts with a dot).

The DESCRIPTION and INDEX files are described in the subsections below. The NAMESPACE

file is described in the section on Section 1.5 [Package namespaces], page 34.

The optional files configure and cleanup are (Bourne shell) script files which are, re-spectively, executed before and (provided that option --clean was given) after installationon Unix-alikes, see Section 1.2 [Configure and cleanup], page 15. The analogues on Windowsare configure.win and cleanup.win.

For the conventions for files NEWS and ChangeLog in the GNU project see http://www.gnu.

org/prep/standards/standards.html#Documentation.

The package subdirectory should be given the same name as the package. Because some filesystems (e.g., those on Windows and by default on OS X) are not case-sensitive, to maintainportability it is strongly recommended that case distinctions not be used to distinguish differentpackages. For example, if you have a package named foo, do not also create a package namedFoo.

To ensure that file names are valid across file systems and supported operating systems, theASCII control characters as well as the characters ‘"’, ‘*’, ‘:’, ‘/’, ‘<’, ‘>’, ‘?’, ‘\’, and ‘|’ are notallowed in file names. In addition, files with names ‘con’, ‘prn’, ‘aux’, ‘clock$’, ‘nul’, ‘com1’ to‘com9’, and ‘lpt1’ to ‘lpt9’ after conversion to lower case and stripping possible “extensions”

2 This seems to be commonly used for a file in ‘markdown’ format. Be aware that most users of R will notknow that, nor know how to view such a file: platforms such as OS X and Windows do not have a defaultviewer set in their file associations. The CRAN package web pages render such files in HTML.

3 currently, top-level files .Rbuildignore and .Rinstignore, and vignettes/.install_extras.

Chapter 1: Creating R packages 4

(e.g., ‘lpt5.foo.bar’), are disallowed. Also, file names in the same directory must not differonly by case (see the previous paragraph). In addition, the basenames of ‘.Rd’ files may be usedin URLs and so must be ASCII and not contain %. For maximal portability filenames should onlycontain only ASCII characters not excluded already (that is A-Za-z0-9._!#$%&+,;=@^(){}’[]— we exclude space as many utilities do not accept spaces in file paths): non-English alphabeticcharacters cannot be guaranteed to be supported in all locales. It would be good practice toavoid the shell metacharacters (){}’[]$~: ~ is also used as part of ‘8.3’ filenames on Windows.In addition, packages are normally distributed as tarballs, and these have a limit on path lengths:for maximal portability 100 bytes.

A source package if possible should not contain binary executable files: they are not portable,and a security risk if they are of the appropriate architecture. R CMD check will warn about them4

unless they are listed (one filepath per line) in a file BinaryFiles at the top level of the package.Note that CRAN will not accept submissions containing binary files even if they are listed.

The R function package.skeleton can help to create the structure for a new package: seeits help page for details.

1.1.1 The DESCRIPTION file

The DESCRIPTION file contains basic information about the package in the following format:� �Package: pkgname

Version: 0.5-1

Date: 2004-01-01

Title: My First Collection of Functions

Authors@R: c(person("Joe", "Developer", role = c("aut", "cre"),

email = "Joe.Developer@some.domain.net"),

person("Pat", "Developer", role = "aut"),

person("A.", "User", role = "ctb",

email = "A.User@whereever.net"))

Author: Joe Developer and Pat Developer, with contributions from A. User

Maintainer: Joe Developer <Joe.Developer@some.domain.net>

Depends: R (>= 1.8.0), nlme

Suggests: MASS

Description: A short (one paragraph) description of what

the package does and why it may be useful.

License: GPL (>= 2)

URL: http://www.r-project.org, http://www.another.url

BugReports: http://pkgname.bugtracker.url The format is that of a version of a ‘Debian Control File’ (see the help for ‘read.dcf’ andhttp://www.debian.org/doc/debian-policy/ch-controlfields.html: R does not requireencoding in UTF-8 and does not support comments starting with ‘#’). Fields start with anASCII name immediately followed by a colon: the value starts after the colon and a space.Continuation lines (for example, for descriptions longer than one line) start with a space or tab.Field names are case-sensitive: all those used by R are capitalized.

For maximal portability, the DESCRIPTION file should be written entirely in ASCII — if thisis not possible it must contain an ‘Encoding’ field (see below).

Several optional fields take logical values: these can be specified as ‘yes’, ‘true’, ‘no’ or‘false’: capitalized values are also accepted.

The ‘Package’, ‘Version’, ‘License’, ‘Description’, ‘Title’, ‘Author’, and ‘Maintainer’fields are mandatory, all other fields are optional. Fields ‘Author’ and ‘Maintainer’ can beauto-generated from ‘Authors@R’, and may be omitted if the latter is provided: however if theyare not ASCII we recommend that they are provided.

4 false positives are possible, but only a handful have been seen so far.

Chapter 1: Creating R packages 5

The mandatory ‘Package’ field gives the name of the package. This should contain only(ASCII) letters, numbers and dot, have at least two characters and start with a letter and notend in a dot.

The mandatory ‘Version’ field gives the version of the package. This is a sequence of atleast two (and usually three) non-negative integers separated by single ‘.’ or ‘-’ characters. Thecanonical form is as shown in the example, and a version such as ‘0.01’ or ‘0.01.0’ will behandled as if it were ‘0.1-0’. It is not a decimal number, so for example 0.9 < 0.75 since 9 <

75.

The mandatory ‘License’ field is discussed in the next subsection.

The mandatory ‘Description’ field should give a comprehensive description of what thepackage does. One can use several (complete) sentences, but only one paragraph.

The mandatory ‘Title’ field should give a short description of the package. Some packagelistings may truncate the title to 65 characters. It should be capitalized, not use any markup,not have any continuation lines, and not end in a period.

The mandatory ‘Author’ field describes who wrote the package. It is a plain text field intendedfor human readers, but not for automatic processing (such as extracting the email addresses ofall listed contributors: for that use ‘Authors@R’). Note that all significant contributors must beincluded: if you wrote an R wrapper for the work of others included in the src directory, youare not the sole (and maybe not even the main) author.

The mandatory ‘Maintainer’ field should give a single name followed a valid (RFC 2822)email address in angle brackets. It should not end in a period or comma. This field is what isreported by the maintainer function and used by bug.report. For a CRAN package it shouldbe a person, not a mailing list and not a corporate entity: do ensure that it is valid and willremain valid for the lifetime of the package.

Both ‘Author’ and ‘Maintainer’ fields can be omitted if a suitable ‘Authors@R’ field is given.This field can be used to provide a refined and machine-readable description of the package“authors” (in particular specifying their precise roles), via suitable R code. The roles can include‘"aut"’ (author) for full authors, ‘"cre"’ (creator) for the package maintainer, and ‘"ctb"’(contributor) for other contributors, ‘"cph"’ (copyright holder), among others. See ?person formore information. Note that no role is assumed by default. Auto-generated package citationinformation takes advantage of this specification. The ‘Author’ and ‘Maintainer’ fields areauto-generated from it if needed when building5 or installing.

An optional ‘Copyright’ field can be used where the copyright holder(s) are not the authors.If necessary, this can refer to an installed file: the convention is to use file inst/COPYRIGHTS.

The ‘Date’ field gives the release date of the current version of the package. It is stronglyrecommended to use the yyyy-mm-dd format conforming to the ISO 8601 standard.

The ‘Depends’, ‘Imports’, ‘Suggests’, ‘Enhances’ and ‘LinkingTo’ fields are discussed in alater subsection.

Dependencies external to the R system should be listed in the ‘SystemRequirements’ field,possibly amplified in a separate README file.

The ‘URL’ field may give a list of URLs separated by commas or whitespace, for examplethe homepage of the author or a page where additional material describing the software can befound. These URLs are converted to active hyperlinks in CRAN package listings.

The ‘BugReports’ field may contain a single URL to which bug reports about the packageshould be submitted. This URL will be used by bug.reports instead of sending an email to themaintainer.

5 at least if this is done in a locale which matches the package encoding.

Chapter 1: Creating R packages 6

Base and recommended packages (i.e., packages contained in the R source distribution oravailable from CRAN and recommended to be included in every binary distribution of R) havea ‘Priority’ field with value ‘base’ or ‘recommended’, respectively. These priorities must notbe used by other packages.

A ‘Collate’ field can be used for controlling the collation order for the R code files in apackage when these are processed for package installation. The default is to collate according tothe ‘C’ locale. If present, the collate specification must list all R code files in the package (tak-ing possible OS-specific subdirectories into account, see Section 1.1.5 [Package subdirectories],page 11) as a whitespace separated list of file paths relative to the R subdirectory. Paths con-taining white space or quotes need to be quoted. An OS-specific collation field (‘Collate.unix’or ‘Collate.windows’) will be used in preference to ‘Collate’.

The ‘LazyData’ logical field controls whether the R datasets use lazy-loading. A ‘LazyLoad’field was used in versions prior to 2.14.0, but now is ignored.

The ‘KeepSource’ logical field controls if the package code is sourced using keep.source =

TRUE or FALSE: it might be needed exceptionally for a package designed to always be used withkeep.source = TRUE.

The ‘ByteCompile’ logical field controls if the package code is to be byte-compiled on in-stallation: the default is currently not to, so this may be useful for a package known to benefitparticularly from byte-compilation (which can take quite a long time and increases the installedsize of the package). It is used for the recommended packages, as they are byte-compiled when Ris installed and for consistency should be byte-compiled when updated. This can be overriddenby installing with flag --no-byte-compile.

The ‘ZipData’ logical field was used to control whether the automatic Windows build wouldzip up the data directory or not prior to R 2.13.0: it is now ignored.

The ‘Biarch’ logical field is used on Windows to select the INSTALL option --force-biarch

for this package. (Introduced in R 3.0.0.)

The ‘BuildVignettes’ logical field can be set to a false value to stop R CMD build fromattempting to build the vignettes, as well as preventing6 R CMD check from testing this. Thisshould only be used exceptionally, for example if the PDFs include large figures which are notpart of the package sources (and hence only in packages which do not have an Open Sourcelicense).

The ‘VignetteBuilder’ field names (in a comma-separated list) packages that provide anengine for building vignettes. These may include the current package, or ones listed in ‘Depends’,‘Suggests’ or ‘Imports’. The utils package is always implicitly appended. See Section 1.4.2[Non-Sweave vignettes], page 33 for details.

If the DESCRIPTION file is not entirely in ASCII it should contain an ‘Encoding’ field specifyingan encoding. This is used as the encoding of the DESCRIPTION file itself and of the R andNAMESPACE files, and as the default encoding of .Rd files. The examples are assumed to be inthis encoding when running R CMD check, and it is used for the encoding of the CITATION file.Only encoding names latin1, latin2 and UTF-8 are known to be portable. (Do not specify anencoding unless one is actually needed: doing so makes the package less portable. If a packagehas a specified encoding, you should run R CMD build etc in a locale using that encoding.)

The ‘NeedsCompilation’ field should be set to "yes" if the package contains code which tobe compiled, otherwise "no" (when the package could be installed from source on any platformwithout additional tools). This is used by install.packages(type = "both") in R >= 2.15.2on platforms where binary packages are the norm: it is normally set by the repository assumingcompilation is required if and only if the package has a src directory.

6 But it is checked for Open Source packages by R CMD check --as-cran.

Chapter 1: Creating R packages 7

The ‘OS_type’ field specifies the OS(es) for which the package is intended. If present, itshould be one of unix or windows, and indicates that the package can only be installed on aplatform with ‘.Platform$OS.type’ having that value.

The ‘Type’ field specifies the type of the package: see Section 1.10 [Package types], page 48.

One can add subject classifications for the content of the package using the fields‘Classification/ACM’ (using the Computing Classification System of the Associationfor Computing Machinery, http: / / www . acm . org / class / ), ‘Classification/JEL’ (theJournal of Economic Literature Classification System, http://www.aeaweb.org/journal/

jel_class_system.html), or ‘Classification/MSC’ (the Mathematics Subject Classificationof the American Mathematical Society, http: / / www . ams . org / msc / ). The subjectclassifications should be comma-separated lists of the respective classification codes, e.g.,‘Classification/ACM: G.4, H.2.8, I.5.1’.

A ‘Language’ field can be used to indicate if the package documentation is not in English:this should be a comma-separated list of standard (not private use or grandfathered) IETFlanguage tags as currently defined by RFC 5646 (http://tools.ietf.org/html/rfc5646, seealso http://en.wikipedia.org/wiki/IETF_language_tag), i.e., use language subtags whichin essence are 2-letter ISO 639-1 (http://en.wikipedia.org/wiki/ISO_639-1) or 3-letter ISO639-3 (http://en.wikipedia.org/wiki/ISO_639-3) language codes.

Note: There should be no ‘Built’ or ‘Packaged’ fields, as these are added by thepackage management tools.

There is no restriction on the use of other fields not mentioned here (but using other capital-izations of these field names would cause confusion). Fields Note, Contact (for contacting theauthors/developers) and MailingList are in common use. Some repositories (including CRAN

and R-forge) add their own fields.

1.1.2 Licensing

Licensing for a package which might be distributed is an important but potentially complexsubject.

It is very important that you include license information! Otherwise, it may not even belegally correct for others to distribute copies of the package, let alone use it.

The package management tools use the concept of ‘free or open source software’ (FOSS, e.g.,http://en.wikipedia.org/wiki/FOSS) licenses: the idea being that some users of R and itspackages want to restrict themselves to such software. Others need to ensure that there are norestrictions stopping them using a package, e.g. forbidding commercial or military use. It is acentral tenet of FOSS software that there are no restrictions on users nor usage.

Do not use the ‘License’ field for information on copyright holders: if needed, use a‘Copyright’ field.

The mandatory ‘License’ field in the DESCRIPTION file should specify the license of the pack-age in a standardized form. Alternatives are indicated via vertical bars. Individual specificationsmust be one of

• One of the “standard” short specifications

GPL-2 GPL-3 LGPL-2 LGPL-2.1 LGPL-3 AGPL-3 Artistic-2.0

BSD_2_clause BSD_3_clause MIT

as made available via http://www.R-project.org/Licenses/ and contained in subdirec-tory share/licenses of the R source or home directory.

• The names or abbreviations of other licenses contained in the license data base in fileshare/licenses/license.db in the R source or home directory, possibly (for versionedlicenses) followed by a version restriction of the form ‘(op v)’ with ‘op’ one of the comparisonoperators ‘<’, ‘<=’, ‘>’, ‘>=’, ‘==’, or ‘!=’ and ‘v’ a numeric version specification (strings of

Chapter 1: Creating R packages 8

non-negative integers separated by ‘.’), possibly combined via ‘,’ (see below for an example).For versioned licenses, one can also specify the name followed by the version, or combinean existing abbreviation and the version with a ‘-’.

Abbreviations GPL and LGPL are ambiguous and usually taken to mean any version of thelicense: but it is better not to use them.

• One of the strings ‘file LICENSE’ or ‘file LICENCE’ referring to a file named LICENSE orLICENCE in the package (source and installation) top-level directory.

• The string ‘Unlimited’, meaning that there are no restrictions on distribution or use otherthan those imposed by relevant laws (including copyright laws).

If a package license restricts a base license (where permitted, e.g., using GPL-3 or AGPL-3with an attribution clause), the additional terms should be placed in file LICENSE (or LICENCE),and the string ‘+ file LICENSE’ (or ‘+ file LICENCE’, respectively) should be appended to thecorresponding individual license specification. Note that several commonly used licenses do notpermit restrictions: this includes GPL-2 and hence any specification which includes it.

Examples of standardized specifications include

License: GPL-2

License: GPL (>= 2) | BSD

License: LGPL (>= 2.0, < 3) | Mozilla Public License

License: GPL-2 | file LICENCE

License: Artistic-2.0 | AGPL-3 + file LICENSE

Please note in particular that “Public domain” is not a valid license, since it is not recognizedin some jurisdictions.

Please ensure that the license you choose also covers any dependencies (including systemdependencies) of your package: it is particularly important that any restrictions on the use ofsuch dependencies are evident to people reading your DESCRIPTION file.

Fields ‘License_is_FOSS’ and ‘License_restricts_use’ may be added by repositorieswhere information cannot be computed from the name of the license. ‘License_is_FOSS: yes’is used for licenses which are known to be FOSS, and ‘License_restricts_use’ can have values‘yes’ or ‘no’ if the LICENSE file is known to restrict users or usage, or known not to. These areused by, e.g., the available.packages filters.

The optional file LICENSE/LICENCE contains a copy of the license of the package. To avoidany confusion only include such a file if it is referred to in the ‘License’ field of the DESCRIPTIONfile.

Whereas you should feel free to include a license file in your source distribution, please donot arrange to install yet another copy of the GNU COPYING or COPYING.LIB files but refer tothe copies on http://www.R-project.org/Licenses/ and included in the R distribution (indirectory share/licenses). Since files named LICENSE or LICENCE will be installed, do not usethese names for standard license files. To include comments about the licensing rather than thebody of a license, use a file named something like LICENSE.note.

A few “standard” licenses are rather license templates which need additional information tobe completed via ‘+ file LICENSE’.

1.1.3 Package Dependencies

The ‘Depends’ field gives a comma-separated list of package names which this package dependson. Those packages will be attached before the current package when library or require iscalled. Each package name may be optionally followed by a comment in parentheses specifyinga version requirement. The comment should contain a comparison operator, whitespace and avalid version number, e.g. ‘MASS (>= 3.1-20)’.

Chapter 1: Creating R packages 9

The ‘Depends’ field can also specify a dependence on a certain version of R — e.g., if thepackage works only with R version 3.0.0 or later, include ‘R (>= 3.0.0)’ in the ‘Depends’ field.You can also require a certain SVN revision for R-devel or R-patched, e.g. ‘R (>= 2.14.0), R

(>= r56550)’ requires a version later than R-devel of late July 2011 (including released versionsof 2.14.0).

It makes no sense to declare a dependence on R without a version specification, nor on thepackage base: this is an R package and package base is always available.

A package or ‘R’ can appear more than once in the ‘Depends’ field, for example to give upperand lower bounds on acceptable versions.

Both library and the R package checking facilities use this field: hence it is an error to useimproper syntax or misuse the ‘Depends’ field for comments on other software that might beneeded. The R INSTALL facilities check if the version of R used is recent enough for the packagebeing installed, and the list of packages which is specified will be attached (after checking versionrequirements) before the current package.

The ‘Imports’ field lists packages whose namespaces are imported from (as specified in theNAMESPACE file) but which do not need to be attached. Namespaces accessed by the ‘::’ and‘:::’ operators must be listed here, or in ‘Suggests’ or ‘Enhances’ (see below). Ideally thisfield will include all the standard packages that are used, and it is important to include S4-usingpackages (as their class definitions can change and the DESCRIPTION file is used to decide whichpackages to re-install when this happens). Packages declared in the ‘Depends’ field should notalso be in the ‘Imports’ field. Version requirements can be specified and are checked when thenamespace is loaded (since R >= 3.0.0).

The ‘Suggests’ field uses the same syntax as ‘Depends’ and lists packages that are not neces-sarily needed. This includes packages used only in examples, tests or vignettes (see Section 1.4[Writing package vignettes], page 31), and packages loaded in the body of functions. E.g., sup-pose an example from package foo uses a dataset from package bar. Then it is not necessaryto have bar use foo unless one wants to execute all the examples/tests/vignettes: it is useful tohave bar, but not necessary. Version requirements can be specified, and will be used by R CMD

check. Note that someone wanting to run the examples/tests/vignettes may not have a sug-gested package available (and it may not even be possible to install it for that platform), so it isdesirable that the use of suggested packages is made conditional via if(require(pkgname))).

Finally, the ‘Enhances’ field lists packages “enhanced” by the package at hand, e.g., byproviding methods for classes from these packages, or ways to handle objects from these packages(so several packages have ‘Enhances: chron’ because they can handle datetime objects fromchron even though they prefer R’s native datetime functions). Version requirements can bespecified, but are currently not used. Such packages cannot be required to check the package:any tests which use them must be conditional on the presence of the package. (If your tests usee.g. a dataset from another package it should be in ‘Suggests’ and not ‘Enhances’.)

The general rules are

• A package should be listed in only one of these fields.

• Packages whose namespace only is needed to load the package using library(pkgname)

should be listed in the ‘Imports’ field and not in the ‘Depends’ field. Packages listedin imports or importFrom directives in the NAMESPACE file should almost always be in‘Imports’ and not ‘Depends’.

• Packages that need to be attached to successfully load the package using library(pkgname)must be listed in the ‘Depends’ field.

• All packages that are needed7 to successfully run R CMD check on the package must belisted in one of ‘Depends’ or ‘Suggests’ or ‘Imports’. Packages used to run examples

7 This includes all packages directly called by library and require calls, as well as data obtained viadata(theirdata, package = "somepkg") calls: R CMD check will warn about all of these. But there are subtler

Chapter 1: Creating R packages 10

or tests conditionally (e.g. via if(require(pkgname))) should be listed in ‘Suggests’ or‘Enhances’. (This allows checkers to ensure that all the packages needed for a completecheck are installed.)

In particular, packages providing “only” data for examples or vignettes should be listed in‘Suggests’ rather than ‘Depends’ in order to make lean installations possible.

Version dependencies in the ‘Depends’ and ‘Imports’ fields are used by library when itloads the package, and install.packages checks versions for the ‘Depends’, ‘Imports’ and (fordependencies = TRUE) ‘Suggests’ fields.

It is increasingly important that the information in these fields is complete and accurate:it is for example used to compute which packages depend on an updated package and whichpackages can safely be installed in parallel.

This scheme was developed before all packages had namespaces (R 2.14.0 in October 2011),and good practice changed once that was in place.

Field ‘Depends’ should nowadays be used rarely, only for packages which are intended to beput on the search path to make their facilities available to the end user (and not to the packageitself): for example it makes sense that a user of package latticeExtra would want the functionsof package lattice made available.

Almost always packages mentioned in ‘Depends’ should also be imported from in theNAMESPACE file: this ensures that any needed parts of those packages are available when someother package imports the current package.

The ‘Imports’ field should not contain packages which are not imported from (via theNAMESPACE file or :: or ::: operators), as all the packages listed in that field need to be installedfor the current package to be installed. (This is checked by R CMD check.)

R code in the package should call library or require only exceptionally. Such calls arenever needed for packages listed in ‘Depends’ as they will already be on the search path. It usedto be common practice to use require calls for packages listed in ‘suggests’ in functions whichused their functionality, but nowadays it is better to access such functionality via :: calls.

A package that wishes to make use of header files in other packages needs to declare them asa comma-separated list in the field ‘LinkingTo’ in the DESCRIPTION file. For example

LinkingTo: link1, link2

As from R 3.0.2 the ‘LinkingTo’ field can have a version requirement which is checked atinstallation. (In earlier versions of R it would cause the specification to be ignored.)

Specifying a package in ‘LinkingTo’ suffices if these are C++ headers containing source codeor static linking is done at installation: the packages do not need to be (and usually should notbe) listed in the ‘Depends’ or ‘Imports’ fields.

For another use of ‘LinkingTo’ see Section 5.4.2 [Linking to native routines in other packages],page 91.

1.1.4 The INDEX file

The optional file INDEX contains a line for each sufficiently interesting object in the package,giving its name and a description (functions such as print methods not usually called explicitlymight not be included). Normally this file is missing and the corresponding information is auto-matically generated from the documentation sources (using tools::Rdindex()) when installingfrom source.

uses which it will not detect: e.g. if package A uses package B and makes use of functionality in package Bwhich uses package C which package B suggests or enhances, then package C needs to be in the ‘Suggests’list for package A. Nor will undeclared uses in included files be reported, nor unconditional uses of packageslisted under ‘Enhances’.

Chapter 1: Creating R packages 11

The file is part of the information given by library(help = pkgname).

Rather than editing this file, it is preferable to put customized information about the packageinto an overview help page (see Section 2.1.4 [Documenting packages], page 56) and/or a vignette(see Section 1.4 [Writing package vignettes], page 31).

1.1.5 Package subdirectories

The R subdirectory contains R code files, only. The code files to be installed must start with anASCII (lower or upper case) letter or digit and have one of the extensions8 .R, .S, .q, .r, or .s.We recommend using .R, as this extension seems to be not used by any other software. It shouldbe possible to read in the files using source(), so R objects must be created by assignments.Note that there need be no connection between the name of the file and the R objects createdby it. Ideally, the R code files should only directly assign R objects and definitely should notcall functions with side effects such as require and options. If computations are required tocreate objects these can use code ‘earlier’ in the package (see the ‘Collate’ field) plus functionsin the ‘Depends’ packages provided that the objects created do not depend on those packagesexcept via namespace imports.

Two exceptions are allowed: if the R subdirectory contains a file sysdata.rda (asaved image of one or more R objects: please use suitable compression as suggested bytools::resaveRdaFiles) this will be lazy-loaded into the namespace environment – this isintended for system datasets that are not intended to be user-accessible via data. Also, filesending in ‘.in’ will be allowed in the R directory to allow a configure script to generatesuitable files.

Only ASCII characters (and the control characters tab, formfeed, LF and CR) should beused in code files. Other characters are accepted in comments, but then the comments may notbe readable in e.g. a UTF-8 locale. Non-ASCII characters in object names will normally9 failwhen the package is installed. Any byte will be allowed in a quoted character string but \uxxxxescapes should be used for non-ASCII characters. However, non-ASCII character strings may notbe usable in some locales and may display incorrectly in others.

Various R functions in a package can be used to initialize and clean up. See Section 1.5.3[Load hooks], page 36.

The man subdirectory should contain (only) documentation files for the objects in the packagein R documentation (Rd) format. The documentation filenames must start with an ASCII (loweror upper case) letter or digit and have the extension .Rd (the default) or .rd. Further, the namesmust be valid in ‘file://’ URLs, which means10 they must be entirely ASCII and not contain‘%’. See Chapter 2 [Writing R documentation files], page 50, for more information. Note that alluser-level objects in a package should be documented; if a package pkg contains user-level objectswhich are for “internal” use only, it should provide a file pkg-internal.Rd which documents allsuch objects, and clearly states that these are not meant to be called by the user. See e.g. thesources for package grid in the R distribution. Note that packages which use internal objectsextensively should not export those objects from their namespace, when they do not need to bedocumented (see Section 1.5 [Package namespaces], page 34).

Having a man directory containing no documentation files may give an installation error.

The R and man subdirectories may contain OS-specific subdirectories named unix or windows.

The sources and headers for the compiled code are in src, plus optionally a file Makevars orMakefile. When a package is installed using R CMD INSTALL, make is used to control compila-

8 Extensions .S and .s arise from code originally written for S(-PLUS), but are commonly used for assemblercode. Extension .q was used for S, which at one time was tentatively called QPE.

9 This is true for OSes which implement the ‘C’ locale: Windows’ idea of the ‘C’ locale uses the WinAnsi charset.10 More precisely, they can contain the English alphanumeric characters and the symbols ‘$ - _ . + ! ’ ( ) , ;

= &’.

Chapter 1: Creating R packages 12

tion and linking into a shared object for loading into R. There are default make variables andrules for this (determined when R is configured and recorded in R_HOME/etcR_ARCH/Makeconf),providing support for C, C++, FORTRAN 77, Fortran 9x11, Objective C and Objective C++12

with associated extensions .c, .cc or .cpp, .f, .f90 or .f95, .m, and .mm, respectively. Werecommend using .h for headers, also for C++13 or Fortran 9x include files. (Use of extension.C for C++ is no longer supported.) Files in the src directory should not be hidden (start witha dot), and hidden files will under some versions of R be ignored.

It is not portable (and may not be possible at all) to mix all these languages in a singlepackage, and we do not support using both C++ and Fortran 9x. Because R itself uses it, weknow that C and FORTRAN 77 can be used together and mixing C and C++ seems to be widelysuccessful.

If your code needs to depend on the platform there are certain defines which can used in Cor C++. On all Windows builds (even 64-bit ones) ‘WIN32’ will be defined: on 64-bit Windowsbuilds also ‘WIN64’, and on OS X ‘__APPLE__’ is defined.14

The default rules can be tweaked by setting macros15 in a file src/Makevars (see Section 1.2.1[Using Makevars], page 18). Note that this mechanism should be general enough to eliminate theneed for a package-specific src/Makefile. If such a file is to be distributed, considerable care isneeded to make it general enough to work on all R platforms. If it has any targets at all, it shouldhave an appropriate first target named ‘all’ and a (possibly empty) target ‘clean’ which removesall files generated by running make (to be used by ‘R CMD INSTALL --clean’ and ‘R CMD INSTALL

--preclean’). There are platform-specific file names on Windows: src/Makevars.win takesprecedence over src/Makevars and src/Makefile.win must be used. Some make programsrequire makefiles to have a complete final line, including a newline.

A few packages use the src directory for purposes other than making a shared object (e.g.to create executables). Such packages should have files src/Makefile and src/Makefile.win

(unless intended for only Unix-alikes or only Windows).

In very special cases packages may create binary files other than the shared objects/DLLsin the src directory. Such files will not be installed in a multi-arch setting since R CMD INSTALL

--libs-only is used to merge multiple architectures and it only copies shared objects/DLLs.If a package wants to install other binaries (for example executable programs), it should pro-vide an R script src/install.libs.R which will be run as part of the installation in the src

build directory instead of copying the shared objects/DLLs. The script is run in a separateR environment containing the following variables: R_PACKAGE_NAME (the name of the package),R_PACKAGE_SOURCE (the path to the source directory of the package), R_PACKAGE_DIR (the pathof the target installation directory of the package), R_ARCH (the arch-dependent part of thepath), SHLIB_EXT (the extension of shared objects) and WINDOWS (TRUE on Windows, FALSEelsewhere). Something close to the default behavior could be replicated with the followingsrc/install.libs.R file:

files <- Sys.glob(paste("*", SHLIB_EXT, sep=’’))

libarch <- if (nzchar(R_ARCH)) paste(’libs’, R_ARCH, sep=’’) else ’libs’

dest <- file.path(R_PACKAGE_DIR, libarch)

dir.create(dest, recursive = TRUE, showWarnings = FALSE)

11 Note that Ratfor is not supported. If you have Ratfor source code, you need to convert it to FORTRAN. OnlyFORTRAN 77 (which we write in upper case) is supported on all platforms, but most also support Fortran-95(for which we use title case). If you want to ship Ratfor source files, please do so in a subdirectory of src andnot in the main subdirectory.

12 either or both of which may not be supported on particular platforms13 Using .hpp is not guaranteed to be portable.14 There is also ‘__APPLE_CC__’, but that indicates a compiler with Apple-specific features, not the OS. It is used

in Rinlinedfuns.h.15 the POSIX terminology, called ‘make variables’ by GNU make.

Chapter 1: Creating R packages 13

file.copy(files, dest, overwrite = TRUE)

if(file.exists("symbols.rds"))

file.copy("symbols.rds", dest, overwrite = TRUE)

The data subdirectory is for data files: See Section 1.1.6 [Data in packages], page 14.

The demo subdirectory is for R scripts (for running via demo()) that demonstrate some ofthe functionality of the package. Demos may be interactive and are not checked automatically,so if testing is desired use code in the tests directory to achieve this. The script files must startwith a (lower or upper case) letter and have one of the extensions .R or .r. If present, the demosubdirectory should also have a 00Index file with one line for each demo, giving its name anda description separated by white space. (Note that it is not possible to generate this index fileautomatically.) Note that a demo does not have a specified encoding and so should be an ASCII

file (see Section 1.6.3 [Encoding issues], page 44). As from R 3.0.0 demo() will use the packageencoding if there is one, but this is mainly useful for non-ASCII comments.

The contents of the inst subdirectory will be copied recursively to the installation directory.Subdirectories of inst should not interfere with those used by R (currently, R, data, demo, exec,libs, man, help, html and Meta, and earlier versions used latex, R-ex). The copying of theinst happens after src is built so its Makefile can create files to be installed. To exclude filesfrom being installed, one can specify a list of exclude patterns in file .Rinstignore in the top-level source directory. These patterns should be Perl-like regular expressions (see the help forregexp in R for the precise details), one per line, to be matched16 against the file and directorypaths, e.g. doc/.*[.]png$ will exclude all PNG files in inst/doc based on the (lower-case)extension.

Note that with the exceptions of INDEX, LICENSE/LICENCE and NEWS, information files at thetop level of the package will not be installed and so not be known to users of Windows and OSX compiled packages (and not seen by those who use R CMD INSTALL or install.packages onthe tarball). So any information files you wish an end user to see should be included in inst.Note that if the named exceptions also occur in inst, the version in inst will be that seen inthe installed package.

Things you might like to add to inst are a CITATION file for use by the citation function,and a NEWS.Rd file for use by the news function.

Another file sometimes needed in inst is AUTHORS or COPYRIGHTS to specify the authors orcopyright holders when this is too complex to put in the DESCRIPTION file.

Subdirectory tests is for additional package-specific test code, similar to the specific teststhat come with the R distribution. Test code can either be provided directly in a .R file, or viaa .Rin file containing code which in turn creates the corresponding .R file (e.g., by collectingall function objects in the package and then calling them with the strangest arguments). Theresults of running a .R file are written to a .Rout file. If there is a corresponding17 .Rout.savefile, these two are compared, with differences being reported but not causing an error. Thedirectory tests is copied to the check area, and the tests are run with the copy as the workingdirectory and with R_LIBS set to ensure that the copy of the package installed during testingwill be found by library(pkg_name). Note that the package-specific tests are run in a vanilla Rsession without setting the random-number seed, so tests which use random numbers will needto set the seed to obtain reproducible results (and it can be helpful to do so in all cases, to avoidoccasional failures when tests are run).

16 case-insensitively on Windows.17 The best way to generate such a file is to copy the .Rout from a successful run of R CMD check. If you want to

generate it separately, do run R with options --vanilla --slave and with environment variable LANGUAGE=enset to get messages in English.

Chapter 1: Creating R packages 14

If tests has a subdirectory Examples containing a file pkg-Ex.Rout.save, this is comparedto the output file for running the examples when the latter are checked. Reference output shouldbe produced without having the --timings option set.

Subdirectory exec could contain additional executable scripts the package needs, typicallyscripts for interpreters such as the shell, Perl, or Tcl. This mechanism is currently used only bya very few packages. NB: only files (and not directories) under exec are installed (and thosewith names starting with a dot are ignored), and they are all marked as executable (mode755, moderated by ‘umask’) on POSIX platforms. Note too that this may not be suitable forexecutable programs since some platforms (including OS X and Windows) support multiplearchitectures using the same installed package directory.

Subdirectory po is used for files related to localization: see Section 1.8 [Internationalization],page 46.

1.1.6 Data in packages

The data subdirectory is for data files, either to be made available via lazy-loading or for loadingusing data(). (The choice is made by the ‘LazyData’ field in the DESCRIPTION file: the defaultis not to do so.) It should not be used for other data files needed by the package, and theconvention has grown up to use directory inst/extdata for such files.

Data files can have one of three types as indicated by their extension: plain R code (.R or.r), tables (.tab, .txt, or .csv, see ?data for the file formats, and note that .csv is not thestandard18 CSV format), or save() images (.RData or .rda). The files should not be hidden(have names starting with a dot). Note that R code should be “self-sufficient” and not make useof extra functionality provided by the package, so that the data file can also be used withouthaving to load the package or its namespace.

Images (extensions .RData or .rda) can contain references to the namespaces of packagesthat were used to create them. Preferably there should be no such references in data files, and inany case they should only be to packages listed in the Depends and Imports fields, as otherwiseit may be impossible to install the package. To check for such references, load all the imagesinto a vanilla R session, and look at the output of loadedNamespaces().

If your data files are large and you are not using ‘LazyData’ you can speed up installationby providing a file datalist in the data subdirectory. This should have one line per topic thatdata() will find, in the format ‘foo’ if data(foo) provides ‘foo’, or ‘foo: bar bah’ if data(foo)provides ‘bar’ and ‘bah’. R CMD build will automatically add a datalist file to data directoriesof over 1Mb, using the function tools::add_datalist.

Tables (.tab, .txt, or .csv files) can be compressed by gzip, bzip2 or xz, optionally withadditional extension .gz, .bz2 or .xz.

If your package is to be distributed, do consider the resource implications of large datasetsfor your users: they can make packages very slow to download and use up unwelcome amountsof storage space, as well as taking many seconds to load. It is normally best to distribute largedatasets as .rda images prepared by save(, compress = TRUE) (the default). Using bzip2 orxz compression will usually reduce the size of both the package tarball and the installed package,in some cases by a factor of two or more.

Package tools has a couple of functions to help with data images: checkRdaFiles reportson the way the image was saved, and resaveRdaFiles will re-save with a different type ofcompression, including choosing the best type for that particular image.

Some packages using ‘LazyData’ will benefit from using a form of compression other thangzip in the installed lazy-loading database. This can be selected by the --data-compress

option to R CMD INSTALL or by using the ‘LazyDataCompression’ field in the DESCRIPTION file.

18 e.g http://tools.ietf.org/html/rfc4180.

Chapter 1: Creating R packages 15

Useful values are bzip2, xz and the default, gzip. The only way to discover which is best is totry them all and look at the size of the pkgname/data/Rdata.rdb file.

Lazy-loading is not supported for very large datasets (those which when serialized exceed2GB, the limit for the format on 32-bit platforms and all platforms prior to R 3.0.0).

1.1.7 Non-R scripts in packages

Code which needs to be compiled (C, C++, FORTRAN, Fortran 95 . . . ) is included in the src

subdirectory and discussed elsewhere in this document.

Subdirectory exec could be used for scripts for interpreters such as the shell (e.g.arulesSequences), BUGS, Java, JavaScript, Matlab, Perl (FEST), php (amap), Python orTcl, or even R. However, it seems more common to use the inst directory, for exampleAMA/inst/java, WriteXLS/inst/Perl, Amelia/inst/tklibs, NMF/inst/matlab andemdbook/inst/BUGS.

If your package requires one of these interpreters or an extension then this should be declaredin the ‘SystemRequirements’ field of its DESCRIPTION file. Windows and Mac users should beaware that the Tcl extensions ‘BWidget’ and ‘Tktable’ which are currently included with theR for Windows and OS X installers are extensions and do need to be declared. ‘Tktable’ didship as part of the X11-based Tcl/Tk provided on CRAN for OS X prior to R 3.0.0, but you willneed to tell your users how to make use of it:

> addTclPath(’/usr/local/lib/Tktable2.9’)

> tclRequire(’Tktable’)

<Tcl> 2.9

It should work with no further user action as from R 3.0.0.

‘BWidget’ needs to be installed by the user for OS X with R 2.x.y and on other OSes. Thisis fairly easy to do: first find the Tcl/Tk search path:

library(tcltk)

strsplit(tclvalue(’auto_path’), " ")[[1]]

then download the sources from http://sourceforge.net/projects/tcllib/files/BWidget/

and at the command line run

tar xf bwidget-1.9.6.tar.gz

sudo mv bwidget-1.9.6 /usr/local/lib

substituting a location on the Tcl/Tk search path for /usr/local/lib if needed.

1.2 Configure and cleanup

Note that most of this section is specific to Unix-alikes: see the comments later on about theWindows port of R.

If your package needs some system-dependent configuration before installation you can in-clude an executable (Bourne shell) script configure in your package which (if present) is ex-ecuted by R CMD INSTALL before any other action is performed. This can be a script createdby the Autoconf mechanism, but may also be a script written by yourself. Use this to detectif any nonstandard libraries are present such that corresponding code in the package can bedisabled at install time rather than giving error messages when the package is compiled or used.To summarize, the full power of Autoconf is available for your extension package (includingvariable substitution, searching for libraries, etc.).

Under a Unix-alike only, an executable (Bourne shell) script cleanup is executed as the lastthing by R CMD INSTALL if option --clean was given, and by R CMD build when preparing thepackage for building from its source.

As an example consider we want to use functionality provided by a (C or FORTRAN) libraryfoo. Using Autoconf, we can create a configure script which checks for the library, sets variable

Chapter 1: Creating R packages 16

HAVE_FOO to TRUE if it was found and to FALSE otherwise, and then substitutes this value intooutput files (by replacing instances of ‘@HAVE_FOO@’ in input files with the value of HAVE_FOO).For example, if a function named bar is to be made available by linking against library foo (i.e.,using -lfoo), one could use

AC_CHECK_LIB(foo, fun, [HAVE_FOO=TRUE], [HAVE_FOO=FALSE])

AC_SUBST(HAVE_FOO)

......

AC_CONFIG_FILES([foo.R])

AC_OUTPUT

in configure.ac (assuming Autoconf 2.50 or later).

The definition of the respective R function in foo.R.in could be

foo <- function(x) {

if(!@HAVE_FOO@)

stop("Sorry, library ’foo’ is not available"))

...

From this file configure creates the actual R source file foo.R looking like

foo <- function(x) {

if(!FALSE)

stop("Sorry, library ’foo’ is not available"))

...

if library foo was not found (with the desired functionality). In this case, the above R codeeffectively disables the function.

One could also use different file fragments for available and missing functionality, respectively.

You will very likely need to ensure that the same C compiler and compiler flags are used inthe configure tests as when compiling R or your package. Under a Unix-alike, you can achievethis by including the following fragment early in configure.ac

: ${R_HOME=‘R RHOME‘}

if test -z "${R_HOME}"; then

echo "could not determine R_HOME"

exit 1

fi

CC=‘"${R_HOME}/bin/R" CMD config CC‘

CFLAGS=‘"${R_HOME}/bin/R" CMD config CFLAGS‘

CPPFLAGS=‘"${R_HOME}/bin/R" CMD config CPPFLAGS‘

(Using ‘${R_HOME}/bin/R’ rather than just ‘R’ is necessary in order to use the correct versionof R when running the script as part of R CMD INSTALL, and the quotes since ‘${R_HOME}’ mightcontain spaces.)

If your code does load checks then you may also need

LDFLAGS=‘"${R_HOME}/bin/R" CMD config LDFLAGS‘

and packages written with C++ need to pick up the details for the C++ compiler and switch thecurrent language to C++ by

AC_LANG(C++)

The latter is important, as for example C headers may not be available to C++ programs or maynot be written to avoid C++ name-mangling.

You can use R CMD config for getting the value of the basic configuration variables, and alsothe header and library flags necessary for linking a front-end executable program against R, seeR CMD config --help for details.

To check for an external BLAS library using the ACX_BLAS macro from the official AutoconfMacro Archive, one can simply do

Chapter 1: Creating R packages 17

F77=‘"${R_HOME}/bin/R" CMD config F77‘

AC_PROG_F77

FLIBS=‘"${R_HOME}/bin/R" CMD config FLIBS‘

ACX_BLAS([], AC_MSG_ERROR([could not find your BLAS library], 1))

Note that FLIBS as determined by R must be used to ensure that FORTRAN 77 code works onall R platforms. Calls to the Autoconf macro AC_F77_LIBRARY_LDFLAGS, which would overwriteFLIBS, must not be used (and hence e.g. removed from ACX_BLAS). (Recent versions of Autoconfin fact allow an already set FLIBS to override the test for the FORTRAN linker flags.)

Note bene: If the configure script creates files, e.g. src/Makevars, you do need a cleanup

script to remove them. Otherwise if the package has vignettes, R CMD build will ship the filesthat are created. For example, package RODBC has

#!/bin/sh

rm -f config.* src/Makevars src/config.h

As this example shows, configure often creates working files such as config.log.

If your configure script needs auxiliary files, it is recommended that you ship them in a toolsdirectory (as R itself does).

You should bear in mind that the configure script will not be used on Windows systems. Ifyour package is to be made publicly available, please give enough information for a user on anon-Unix-alike platform to configure it manually, or provide a configure.win script to be usedon that platform. (Optionally, there can be a cleanup.win script. Both should be shell scriptsto be executed by ash, which is a minimal version of Bourne-style sh.) When configure.win

is run the environment variables R_HOME (which uses ‘/’ as the file separator), R_ARCH and UseR_ARCH_BIN will be set. Use R_ARCH to decide if this is a 64-bit build (its value there is ‘/x64’)and to install DLLs to the correct place (${R_HOME}/libs${R_ARCH}). Use R_ARCH_BIN to findthe correct place under the bin directory, e.g. ${R_HOME}/bin${R_ARCH_BIN}/Rscript.exe.

In some rare circumstances, the configuration and cleanup scripts need to know the locationinto which the package is being installed. An example of this is a package that uses C codeand creates two shared object/DLLs. Usually, the object that is dynamically loaded by R islinked against the second, dependent, object. On some systems, we can add the location of thisdependent object to the object that is dynamically loaded by R. This means that each user doesnot have to set the value of the LD_LIBRARY_PATH (or equivalent) environment variable, butthat the secondary object is automatically resolved. Another example is when a package installssupport files that are required at run time, and their location is substituted into an R datastructure at installation time. (This happens with the Java Archive files in the Omegahat SJavapackage.) The names of the top-level library directory (i.e., specifiable via the ‘-l’ argument)and the directory of the package itself are made available to the installation scripts via the twoshell/environment variables R_LIBRARY_DIR and R_PACKAGE_DIR. Additionally, the name of thepackage (e.g. ‘survival’ or ‘MASS’) being installed is available from the environment variableR_PACKAGE_NAME. (Currently the value of R_PACKAGE_DIR is always ${R_LIBRARY_DIR}/${R_

PACKAGE_NAME}, but this used not to be the case when versioned installs were allowed. Its mainuse is in configure.win scripts for the installation path of external software’s DLLs.) Notethat the value of R_PACKAGE_DIR may contain spaces and other shell-unfriendly characters, andso should be quoted in makefiles and configure scripts.

One of the more tricky tasks can be to find the headers and libraries of external software.One tool which is increasingly available on Unix-alikes (but not by default on OS X) to do thisis pkg-config. The configure script will need to test for the presence of the command itself(see for example package Cairo), and if present it can be asked if the software is installed, of asuitable version and for compilation/linking flags by e.g.

$ pkg-config --exists ’QtCore >= 4.0.0’ # check the status

Chapter 1: Creating R packages 18

$ pkg-config --modversion QtCore

4.7.1

$ pkg-config --cflags QtCore

-DQT_SHARED -I/usr/include/QtCore

$ pkg-config --libs QtCore

-lQtCore

Note that pkg-config --libs gives the information required to link against the default versionof that library (usually the dynamic one), and pkg-config --static is needed if the staticlibrary is to be used.

Sometimes the name by which the software is known to pkg-config is not what one mightexpect (e.g. ‘gtk+-2.0’ even for 2.22). To get a complete list use

pkg-config --list-all | sort

1.2.1 Using Makevars

Sometimes writing your own configure script can be avoided by supplying a file Makevars: alsoone of the most common uses of a configure script is to make Makevars from Makevars.in.

A Makevars file is a makefile and is used as one of several makefiles by R CMD SHLIB (whichis called by R CMD INSTALL to compile code in the src directory). It should be written if at allpossible in a portable style, in particular (except for Makevars.win) without the use of GNUextensions.

The most common use of a Makevars file is to set additional preprocessor options (for exampleinclude paths) for C/C++ files via PKG_CPPFLAGS, and additional compiler flags by setting PKG_

CFLAGS, PKG_CXXFLAGS, PKG_FFLAGS or PKG_FCFLAGS, for C, C++, FORTRAN or Fortran 9xrespectively (see Section 5.5 [Creating shared objects], page 92).

NB: Include paths are preprocessor options, not compiler options, and must be set in PKG_

CPPFLAGS as otherwise platform-specific paths (e.g. ‘-I/usr/local/include’) will take prece-dence.

Makevars can also be used to set flags for the linker, for example ‘-L’ and ‘-l’ options, viaPKG_LIBS.

When writing a Makevars file for a package you intend to distribute, take care to ensure thatit is not specific to your compiler: flags such as -O2 -Wall -pedantic (and all other -W flags:for the Solaris compiler these are used to pass arguments to compiler phases) are all specific toGCC.

Also, do not set variables such as CPPFLAGS, CFLAGS etc.: these should be settable by users(sites) through appropriate personal (site-wide) Makevars files. See Section “Customizing pack-age compilation” in R Installation and Administration,

There are some macros19 which are set whilst configuring the building of R itself andare stored in R_HOME/etcR_ARCH/Makeconf. That makefile is included as a Makefile afterMakevars[.win], and the macros it defines can be used in macro assignments and make com-mand lines in the latter. These include

FLIBS A macro containing the set of libraries need to link FORTRAN code. This mayneed to be included in PKG_LIBS: it will normally be included automatically if thepackage contains FORTRAN source files.

BLAS_LIBS

A macro containing the BLAS libraries used when building R. This may need tobe included in PKG_LIBS. Beware that if it is empty then the R executable will

19 in POSIX parlance: GNU make calls these ‘make variables’.

Chapter 1: Creating R packages 19

contain all the double-precision and double-complex BLAS routines, but no single-precision or complex routines. If BLAS_LIBS is included, then FLIBS also needs tobe20 included following it, as most BLAS libraries are written at least partially inFORTRAN.

LAPACK_LIBS

A macro containing the LAPACK libraries (and paths where appropriate) used whenbuilding R. This may need to be included in PKG_LIBS. It may point to a dynamiclibrary libRlapack which contains all the double-precision LAPACK routines aswell as those double-complex LAPACK and BLAS routines needed to build R, or itmay point to an external LAPACK library, or may be empty if an external BLASlibrary also contains LAPACK.

[There is no guarantee that the LAPACK library will provide more than all thedouble-precision and double-complex routines, and some do not provide all the aux-iliary routines.]

For portability, the macros BLAS_LIBS and FLIBS should always be included afterLAPACK_LIBS (and in that order).

SAFE_FFLAGS

A macro containing flags which are needed to circumvent over-optimization of FOR-TRAN code: it is typically ‘-g -O2 -ffloat-store’ on ‘ix86’ platforms usinggfortran. Note that this is not an additional flag to be used as part of PKG_

FFLAGS, but a replacement for FFLAGS, and that it is intended for the FORTRAN77 compiler ‘F77’ and not necessarily for the Fortran 90/95 compiler ‘FC’. See theexample later in this section.

Setting certain macros in Makevars will prevent R CMD SHLIB setting them: in particular ifMakevars sets ‘OBJECTS’ it will not be set on the make command line. This can be useful inconjunction with implicit rules to allow other types of source code to be compiled and includedin the shared object. It can also be used to control the set of files which are compiled, either byexcluding some files in src or including some files in subdirectories. For example

OBJECTS = 4dfp/endianio.o 4dfp/Getifh.o R4dfp-object.o

Note that Makevars should not normally contain targets, as it is included before the defaultmakefile and make will call the first target, intended to be all in the default makefile. If youreally need to circumvent that, use a suitable (phony) target all before any actual targets inMakevars.[win]: for example package fastICA used to have

PKG_LIBS = @BLAS_LIBS@

SLAMC_FFLAGS=$(R_XTRA_FFLAGS) $(FPICFLAGS) $(SHLIB_FFLAGS) $(SAFE_FFLAGS)

all: $(SHLIB)

slamc.o: slamc.f

$(F77) $(SLAMC_FFLAGS) -c -o slamc.o slamc.f

needed to ensure that the LAPACK routines find some constants without infinite looping. TheWindows equivalent was

all: $(SHLIB)

slamc.o: slamc.f

20 at least on Unix-alikes: the Windows build currently resolves such dependencies to a static FORTRAN librarywhen Rblas.dll is built.

Chapter 1: Creating R packages 20

$(F77) $(SAFE_FFLAGS) -c -o slamc.o slamc.f

(since the other macros are all empty on that platform, and R’s internal BLAS was not used).Note that the first target in Makevars will be called, but for back-compatibility it is best namedall.

If you want to create and then link to a library, say using code in a subdirectory, use somethinglike

.PHONY: all mylibs

all: $(SHLIB)

$(SHLIB): mylibs

mylibs:

(cd subdir; make)

Be careful to create all the necessary dependencies, as there is a no guarantee that the depen-dencies of all will be run in a particular order (and some of the CRAN build machines usemultiple CPUs and parallel makes).

Note that on Windows it is required that Makevars[.win] does create a DLL: this is neededas it is the only reliable way to ensure that building a DLL succeeded. If you want to use thesrc directory for some purpose other than building a DLL, use a Makefile.win file.

It is sometimes useful to have a target ‘clean’ in Makevars or Makevars.win: this will beused by R CMD build to clean up (a copy of) the package sources. When it is run by build itwill have fewer macros set, in particular not $(SHLIB), nor $(OBJECTS) unless set in the fileitself. It would also be possible to add tasks to the target ‘shlib-clean’ which is run by R CMD

INSTALL and R CMD SHLIB with options --clean and --preclean.

If you want to run R code in Makevars, e.g. to find configuration information, please doensure that you use the correct copy of R or Rscript: there might not be one in the path at all,or it might be the wrong version or architecture. The correct way to do this is via

"$(R_HOME)/bin$(R_ARCH_BIN)/Rscript" filename

"$(R_HOME)/bin$(R_ARCH_BIN)/Rscript" -e ’R expression’

where $(R_ARCH_BIN) is only needed currently on Windows.

Environment or make variables can be used to select different macros for 32- and 64-bit code,for example (GNU make syntax, allowed on Windows)

ifeq "$(WIN)" "64"

PKG_LIBS = value for 64-bit Windows

else

PKG_LIBS = value for 32-bit Windows

endif

On Windows there is normally a choice between linking to an import library or directly toa DLL. Where possible, the latter is much more reliable: import libraries are tied to a specifictoolchain, and in particular on 64-bit Windows two different conventions have been commonlyused. So for example instead of

PKG_LIBS = -L$(XML_DIR)/lib -lxml2

one can use

PKG_LIBS = -L$(XML_DIR)/bin -lxml2

since on Windows -lxxx will look in turn for

libxxx.dll.a

xxx.dll.a

libxxx.a

Chapter 1: Creating R packages 21

xxx.lib

libxxx.dll

xxx.dll

where the first and second are conventionally import libraries, the third and fourth often staticlibraries (with .lib intended for Visual C++), but might be import libraries. See for examplehttp://sourceware.org/binutils/docs-2.20/ld/WIN32.html#WIN32.

The fly in the ointment is that the DLL might not be named libxxx.dll, and in fact on32-bit Windows there is a libxml2.dll whereas on one build for 64-bit Windows the DLL iscalled libxml2-2.dll. Using import libraries can cover over these differences but can causeequal difficulties.

If static libraries are available they can save a lot of problems with run-time finding of DLLs,especially when binary packages are to be distributed and even more when these support botharchitectures. Where using DLLs is unavoidable we normally arrange (via configure.win) toship them in the same directory as the package DLL.

1.2.1.1 OpenMP support

There is some support for packages which wish to use OpenMP21. The make macros

SHLIB_OPENMP_CFLAGS

SHLIB_OPENMP_CXXFLAGS

SHLIB_OPENMP_FCFLAGS

SHLIB_OPENMP_FFLAGS

are available for use in src/Makevars or src/Makevars.win. Include the appropriate macroin PKG_CFLAGS, PKG_CPPFLAGS and so on, and also in PKG_LIBS. C/C++ code that needs tobe conditioned on the use of OpenMP can be used inside #ifdef SUPPORT_OPENMP, a macrodefined in the header Rconfig.h (see Section 6.13 [Platform and version information], page 132)or _OPENMP: note that some toolchains used for R (e.g. clang) have no OpenMP support at all,not even omp.h.

For example, a package with C code written for OpenMP should have in src/Makevars thelines

PKG_CFLAGS = $(SHLIB_OPENMP_CFLAGS)

PKG_LIBS = $(SHLIB_OPENMP_CFLAGS)

There is nothing to say what version of OpenMP is supported: version 3.0 (May 2008)is supported by recent versions of the Linux, Windows and Solaris platforms, but portablepackages cannot assume that end users have recent versions. The compilers currently used onOS X have partial support for OpenMP 2.5, but this is not enabled in the CRAN build. (Applehave discontinued support for those compilers, and their preferred alternative, clang, has noOpenMP support.)

The performance of OpenMP varies substantially between platforms. Both the Windows andOS X (where available) implementations have substantial overheads and are only beneficial ifquite substantial tasks are run in parallel.

Calling any of the R API from threaded code is ‘for experts only’: they will need to readthe source code to determine if it is thread-safe. In particular, code which makes use of thestack-checking mechanism must not be called from threaded code.

Packages are not standard-alone programs, and an R process could contain more than oneOpenMP-enabled package as well as other components (for example, an optimized BLAS) mak-ing use of OpenMP. So careful consideration needs to be given to resource usage. OpenMPworks with parallel regions, and for most implementations the default is to use as many threads

21 http://www.openmp.org/ , http://en.wikipedia.org/wiki/OpenMP, https://computing.llnl.gov/

tutorials/openMP/

Chapter 1: Creating R packages 22

as ‘CPUs’ for such regions. Parallel regions can be nested, although it is common to use onlya single thread below the first level. The correctness of the detected number of ‘CPUs’ and theassumption that the R process is entitled to use them all are both dubious assumptions. Thebest way to limit resources is to limit the overall number of threads available to OpenMP in theR process: this can be done via environment variable OMP_THREAD_LIMIT, where implemented.22

Alternatively, the number of threads per region can be limited by the environment variable OMP_NUM_THREADS or API call omp_set_num_threads, or, better, for the regions in your code as partof their specification. E.g. R uses

#pragma omp parallel for num_threads(nthreads) ...

That way you only control your own code and not that of other OpenMP users.

1.2.1.2 Using pthreads

There is no direct support for the POSIX threads (more commonly known as pthreads): bythe time we considered adding it several packages were using it unconditionally so it seems thatnowadays it is universally available on POSIX operating systems (hence not Windows).

For reasonably recent versions of gcc and clang the correct specification is

PKG_CPPFLAGS = -pthread

PKG_LIBS = -pthread

(and the plural version is also accepted on some systems/versions). For other platforms thespecification is

PKG_CPPFLAGS = -D_REENTRANT

PKG_LIBS = -lpthread

(and note that the library name is singular). This is what -pthread does on all known currentplatforms (although earlier versions of OpenBSD used a different library name).

For a tutorial see https://computing.llnl.gov/tutorials/pthreads/.

POSIX threads are not normally used on Windows, which has its own native conceptsof threads. However, there are two projects implementing pthreads on top of Windows,pthreads-w32 and winpthreads (a recent part of the MinGW-w64 project).

Whether Windows toolchains implement pthreads is up to the toolchain provider: the cur-rently recommended toolchain does by default provide it. A make variable SHLIB_PTHREAD_

FLAGS is available: this should be included in both PKG_CPPFLAGS (or the Fortran or F9x equiv-alents) and PKG_LIBS.

The presence of a working pthreads implementation cannot be unambiguously determinedwithout testing for yourself: however, that ‘_REENTRANT’ is defined23 in C/C++ code is a goodindication.

See also the comments on thread-safety and performance under OpenMP: on all known Rplatforms OpenMP is implemented via pthreads and the known performance issues are in thelatter.

1.2.1.3 Compiling in sub-directories

Package authors fairly often want to organize code in sub-directories of src, for example if theyare including a separate piece of external software to which this is an R interface.

One simple way is simply to set OBJECTS to be all the objects that need to be compiled,including in sub-directories. For example, CRAN package RSiena has

22 Which it was at the time of writing with GCC, Solaris Studio and Intel compilers.23 some Windows toolchains have the typo ‘_REENTRANCE’ instead.

Chapter 1: Creating R packages 23

SOURCES = $(wildcard data/*.cpp network/*.cpp utils/*.cpp model/*.cpp model/*/*.cpp model/*/*/*.cpp)

OBJECTS = siena07utilities.o siena07internals.o siena07setup.o siena07models.o $(SOURCES:.cpp=.o)

One problem with that approach is that unless GNU make extensions are used, the source filesneed to be listed and kept up-to-date. As in the following from CRAN package lossDev:

OBJECTS.samplers = samplers/ExpandableArray.o samplers/Knots.o \

samplers/RJumpSpline.o samplers/RJumpSplineFactory.o \

samplers/RealSlicerOV.o samplers/SliceFactoryOV.o samplers/MNorm.o

OBJECTS.distributions = distributions/DSpline.o \

distributions/DChisqrOV.o distributions/DTOV.o \

distributions/DNormOV.o distributions/DUnifOV.o distributions/RScalarDist.o

OBJECTS.root = RJump.o

OBJECTS = $(OBJECTS.samplers) $(OBJECTS.distributions) $(OBJECTS.root)

Where the subdirectory is self-contained code with a suitable makefile, the best approach issomething like

PKG_LIBS = -LCsdp/lib -lsdp $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS)

$(SHLIB): Csdp/lib/libsdp.a

Csdp/lib/libsdp.a

@(cd Csdp/lib && $(MAKE) libsdp.a \

CC="$(CC)" CFLAGS="$(CFLAGS) $(CPICFLAGS)" AR="$(AR)" RANLIB="$(RANLIB)")

Note the quotes: the macros can contain spaces, e.g. CC = "gcc -m64 -std=gnu99". Severalauthors have forgotten about parallel makes: the static library in the subdirectory must bemade before the shared object ($(SHLIB)) and so the latter must depend on the former. Othersforget the need for position-independent code.

We really do not recommend using src/Makefile instead of src/Makevars, and as theexample above shows, it is not necessary.

1.2.2 Configure example

It may be helpful to give an extended example of using a configure script to create asrc/Makevars file: this is based on that in the RODBC package.

The configure.ac file follows: configure is created from this by running autoconf in thetop-level package directory (containing configure.ac).

AC_INIT([RODBC], 1.1.8) dnl package name, version

dnl A user-specifiable option

odbc_mgr=""

AC_ARG_WITH([odbc-manager],

AC_HELP_STRING([--with-odbc-manager=MGR],

[specify the ODBC manager, e.g. odbc or iodbc]),

[odbc_mgr=$withval])

if test "$odbc_mgr" = "odbc" ; then

AC_PATH_PROGS(ODBC_CONFIG, odbc_config)

fi

dnl Select an optional include path, from a configure option

dnl or from an environment variable.

AC_ARG_WITH([odbc-include],

AC_HELP_STRING([--with-odbc-include=INCLUDE_PATH],

[the location of ODBC header files]),

[odbc_include_path=$withval])

RODBC_CPPFLAGS="-I."

if test [ -n "$odbc_include_path" ] ; then

RODBC_CPPFLAGS="-I. -I${odbc_include_path}"

else

Chapter 1: Creating R packages 24

if test [ -n "${ODBC_INCLUDE}" ] ; then

RODBC_CPPFLAGS="-I. -I${ODBC_INCLUDE}"

fi

fi

dnl ditto for a library path

AC_ARG_WITH([odbc-lib],

AC_HELP_STRING([--with-odbc-lib=LIB_PATH],

[the location of ODBC libraries]),

[odbc_lib_path=$withval])

if test [ -n "$odbc_lib_path" ] ; then

LIBS="-L$odbc_lib_path ${LIBS}"

else

if test [ -n "${ODBC_LIBS}" ] ; then

LIBS="-L${ODBC_LIBS} ${LIBS}"

else

if test -n "${ODBC_CONFIG}"; then

odbc_lib_path=‘odbc_config --libs | sed s/-lodbc//‘

LIBS="${odbc_lib_path} ${LIBS}"

fi

fi

fi

dnl Now find the compiler and compiler flags to use

: ${R_HOME=‘R RHOME‘}

if test -z "${R_HOME}"; then

echo "could not determine R_HOME"

exit 1

fi

CC=‘"${R_HOME}/bin/R" CMD config CC‘

CPP=‘"${R_HOME}/bin/R" CMD config CPP‘

CFLAGS=‘"${R_HOME}/bin/R" CMD config CFLAGS‘

CPPFLAGS=‘"${R_HOME}/bin/R" CMD config CPPFLAGS‘

AC_PROG_CC

AC_PROG_CPP

if test -n "${ODBC_CONFIG}"; then

RODBC_CPPFLAGS=‘odbc_config --cflags‘

fi

CPPFLAGS="${CPPFLAGS} ${RODBC_CPPFLAGS}"

dnl Check the headers can be found

AC_CHECK_HEADERS(sql.h sqlext.h)

if test "${ac_cv_header_sql_h}" = no ||

test "${ac_cv_header_sqlext_h}" = no; then

AC_MSG_ERROR("ODBC headers sql.h and sqlext.h not found")

fi

dnl search for a library containing an ODBC function

if test [ -n "${odbc_mgr}" ] ; then

AC_SEARCH_LIBS(SQLTables, ${odbc_mgr}, ,

AC_MSG_ERROR("ODBC driver manager ${odbc_mgr} not found"))

else

AC_SEARCH_LIBS(SQLTables, odbc odbc32 iodbc, ,

AC_MSG_ERROR("no ODBC driver manager found"))

fi

dnl for 64-bit ODBC need SQL[U]LEN, and it is unclear where they are defined.

AC_CHECK_TYPES([SQLLEN, SQLULEN], , , [# include <sql.h>])

dnl for unixODBC header

AC_CHECK_SIZEOF(long, 4)

dnl substitute RODBC_CPPFLAGS and LIBS

AC_SUBST(RODBC_CPPFLAGS)

Chapter 1: Creating R packages 25

AC_SUBST(LIBS)

AC_CONFIG_HEADERS([src/config.h])

dnl and do substitution in the src/Makevars.in and src/config.h

AC_CONFIG_FILES([src/Makevars])

AC_OUTPUT

where src/Makevars.in would be simply

PKG_CPPFLAGS = @RODBC_CPPFLAGS@

PKG_LIBS = @LIBS@

A user can then be advised to specify the location of the ODBC driver manager files byoptions like (lines broken for easier reading)

R CMD INSTALL \

--configure-args=’--with-odbc-include=/opt/local/include \

--with-odbc-lib=/opt/local/lib --with-odbc-manager=iodbc’ \

RODBC

or by setting the environment variables ODBC_INCLUDE and ODBC_LIBS.

1.2.3 Using F95 code

R assumes that source files with extension .f are FORTRAN 77, and passes them to the compilerspecified by ‘F77’. On most but not all platforms that compiler will accept Fortran 90/95 code:some platforms have a separate Fortran 90/95 compiler and a few (by now quite rare24) platformshave no Fortran 90/95 support.

This means that portable packages need to be written in correct FORTRAN 77, which willalso be valid Fortran 95. See http://developer.R-project.org/Portability.html forreference resources. In particular, free source form F95 code is not portable.

On some systems an alternative F95 compiler is available: from the gcc family this mightbe gfortran or g95. Configuring R will try to find a compiler which (from its name) appearsto be a Fortran 90/95 compiler, and set it in macro ‘FC’. Note that it does not check that sucha compiler is fully (or even partially) compliant with Fortran 90/95. Packages making use ofFortran 90/95 features should use file extension .f90 or .f95 for the source files: the variablePKG_FCFLAGS specifies any special flags to be used. There is no guarantee that compiled Fortran90/95 code can be mixed with any other type of compiled code, nor that a build of R will havesupport for such packages.

Some (but not) all compilers specified by the ‘FC’ macro will accept Fortran 2003 or 2008code: such code should still use file extension .f90 or .f95. For platforms using gfortran, youmay need to include -std=f2003 or -std=f2008 in PKG_FCFLAGS: the default is ‘GNU Fortran’,Fortran 95 with non-standard extensions. The Solaris f95 compiler ‘accepts some Fortran 2003features’. Note that the compiler used for OS X is gfortran 4.2.3 which has limited Fortran2003 support (http://gcc.gnu.org/onlinedocs/gcc-4.2.3/gfortran/).

1.3 Checking and building packages

Before using these tools, please check that your package can be installed (which checked it canbe loaded). R CMD check will inter alia do this, but you may get more detailed error messagesdoing the install directly.

If your package specifies an encoding in its DESCRIPTION file, you should run these tools in alocale which makes use of that encoding: they may not work at all or may work incorrectly inother locales (although UTF-8 locales will most likely work).

Note: R CMD check and R CMD build run R processes with --vanilla in whichnone of the user’s startup files are read. If you need R_LIBS set (to find packages

24 Cygwin used g77 up to 2011, and some pre-built versions of R for Unix OSes still do.

Chapter 1: Creating R packages 26

in a non-standard library) you can set it in the environment: also you can usethe check and build environment files (as specified by the environment variablesR_CHECK_ENVIRON and R_BUILD_ENVIRON; if unset, files25 ~/.R/check.Renviron

and ~/.R/build.Renviron are used) to set environment variables when using theseutilities.

Note to Windows users: R CMD build may make use of the Windows toolset (see the“R Installation and Administration” manual) if present and in your path, and it isrequired for packages which need it to install (including those with configure.win

or cleanup.win scripts or a src directory) and e.g. need vignettes built.

You may need to set the environment variable TMPDIR to point to a suitable writabledirectory with a path not containing spaces – use forward slashes for the separators.Also, the directory needs to be on a case-honouring file system (some network-mounted file systems are not).

1.3.1 Checking packages

Using R CMD check, the R package checker, one can test whether source R packages work cor-rectly. It can be run on one or more directories, or compressed package tar archives withextension .tar.gz, .tgz, .tar.bz2 or .tar.xz.

It is strongly recommended that the final checks are run on a tar archive prepared by R CMD

build.

This runs a series of checks, including

1. The package is installed. This will warn about missing cross-references and duplicate aliasesin help files.

2. The file names are checked to be valid across file systems and supported operating systemplatforms.

3. The files and directories are checked for sufficient permissions (Unix-alikes only).

4. The files are checked for binary executables, using a suitable version of file if available26.(There may be rare false positives.)

5. The DESCRIPTION file is checked for completeness, and some of its entries for correctness.Unless installation tests are skipped, checking is aborted if the package dependencies cannotbe resolved at run time. (You may need to set R_LIBS in the environment if dependentpackages are in a separate library tree.) One check is that the package name is not that ofa standard package, nor one of the defunct standard packages (‘ctest’, ‘eda’, ‘lqs’, ‘mle’,‘modreg’, ‘mva’, ‘nls’, ‘stepfun’ and ‘ts’). Another check is that all packages mentionedin library or requires or from which the NAMESPACE file imports or are called via :: or::: are listed (in ‘Depends’, ‘Imports’, ‘Suggests’): this is not an exhaustive check of theactual imports.

6. Available index information (in particular, for demos and vignettes) is checked for com-pleteness.

7. The package subdirectories are checked for suitable file names and for not being empty. Thechecks on file names are controlled by the option --check-subdirs=value. This defaults to‘default’, which runs the checks only if checking a tarball: the default can be overriddenby specifying the value as ‘yes’ or ‘no’. Further, the check on the src directory is onlyrun if the package does not contain a configure script (which corresponds to the value‘yes-maybe’) and there is no src/Makefile or src/Makefile.in.

25 On systems which use sub-architectures, architecture-specific versions such as ~/.R/check.Renviron.i386

take precedence.26 A suitable file.exe is part of the Windows toolset: it checks for gfile if a suitable file is not found: the

latter is available in the OpenCSW collection for Solaris at http://www.opencsw.org.

Chapter 1: Creating R packages 27

To allow a configure script to generate suitable files, files ending in ‘.in’ will be allowedin the R directory.

A warning is given for directory names that look like R package check directories – manypackages have been submitted to CRAN containing these.

8. The R files are checked for syntax errors. Bytes which are non-ASCII are reported aswarnings, but these should be regarded as errors unless it is known that the package willalways be used in the same locale.

9. It is checked that the package can be loaded, first with the usual default packages and thenonly with package base already loaded. It is checked that the namespace this can be loadedin an empty session with only the base namespace loaded. (Namespaces and packages canbe loaded very early in the session, before the default packages are available, so packagesshould work then.)

10. The R files are checked for correct calls to library.dynam. Package startup functions arechecked for correct argument lists and (incorrect) calls to functions which modify the searchpath or inappropriately generate messages. The R code is checked for possible problemsusing codetools. In addition, it is checked whether S3 methods have all arguments ofthe corresponding generic, and whether the final argument of replacement functions iscalled ‘value’. All foreign function calls (.C, .Fortran, .Call and .External calls) aretested to see if they have a PACKAGE argument, and if not, whether the appropriate DLLmight be deduced from the namespace of the package. Any other calls are reported. (Thecheck is generous, and users may want to supplement this by examining the output oftools::checkFF("mypkg", verbose=TRUE), especially if the intention were to always usea PACKAGE argument)

11. The Rd files are checked for correct syntax and metadata, including the presence of themandatory fields (\name, \alias, \title and \description). The Rd name and title arechecked for being non-empty, and there is a check for missing cross-references (links).

12. A check is made for missing documentation entries, such as undocumented user-level objectsin the package.

13. Documentation for functions, data sets, and S4 classes is checked for consistency with thecorresponding code.

14. It is checked whether all function arguments given in \usage sections of Rd files are docu-mented in the corresponding \arguments section.

15. The data directory is checked for non-ASCII characters and for the use of reasonable levelsof compression.

16. C, C++ and FORTRAN source and header files27 are tested for portable (LF-only) lineendings. If there is a Makefile or Makefile.in or Makevars or Makevars.in file under thesrc directory, it is checked for portable line endings and the correct use of ‘$(BLAS_LIBS)’and ‘$(LAPACK_LIBS)’

Compiled code is checked for symbols corresponding to functions which might terminateR or write to stdout/stderr instead of the console. Note that the latter might give falsepositives in that the symbols might be pulled in with external libraries and could neverbe called. Windows28 users should note that the Fortran and C++ runtime libraries areexamples of such external libraries.

17. Some checks are made of the contents of the inst/doc directory. These always includechecking for files that look like leftovers, and if suitable tools (such as qpdf) are available,checking that the PDF documentation is of minimal size.

27 An exception is made for subdirectories with names starting ‘win’ or ‘Win’.28 on most other platforms such runtime libraries are dynamic, but static libraries are currently used on Windows

because the toolchain is not a standard part of the OS.

Chapter 1: Creating R packages 28

18. The examples provided by the package’s documentation are run. (see Chapter 2 [WritingR documentation files], page 50, for information on using \examples to create executableexample code.) If there is a file tests/Examples/pkg-Ex.Rout.save, the output of runningthe examples is compared to that file.

Of course, released packages should be able to run at least their own examples. Eachexample is run in a ‘clean’ environment (so earlier examples cannot be assumed to havebeen run), and with the variables T and F redefined to generate an error unless they are setin the example: See Section “Logical vectors” in An Introduction to R.

19. If the package sources contain a tests directory then the tests specified in that directoryare run. (Typically they will consist of a set of .R source files and target output files.Rout.save.) Please note that the comparison will be done in the end user’s locale, so thetarget output files should be ASCII if at all possible.

20. The code in package vignettes (see Section 1.4 [Writing package vignettes], page 31) isexecuted, and the vignette PDFs re-made from their sources as a check of completeness ofthe sources (unless there is a ‘BuildVignettes’ field in the package’s DESCRIPTION file witha false value). If there is a target output file .Rout.save in the vignette source directory,the output from running the code in that vignette is compared with the target output fileand any differences are reported (but not recorded in the log file). (If the vignette sourcesare in the deprecated location inst/doc, do mark such target output files to not be installedin .Rinstignore.)

If there is an error29 in executing the R code in vignette foo.ext, a log file foo.ext.log

is created in the check directory. The vignette PDFs are re-made in a copy of the packagesources in the vign_test subdirectory of the check directory, so for further information onerrors look in directory pkgname/vign_test/vignettes. (It is only retained if there areerrors or if environment variable _R_CHECK_CLEAN_VIGN_TEST_ is set to a false value.)

21. The PDF version of the package’s manual is created (to check that the Rd files can beconverted successfully). This needs LATEX and suitable fonts and LATEX packages to beinstalled. See Section “Making the manuals” in R Installation and Administration.

All these tests are run with collation set to the C locale, and for the examples and tests withenvironment variable LANGUAGE=en: this is to minimize differences between platforms.

Use R CMD check --help to obtain more information about the usage of the R packagechecker. A subset of the checking steps can be selected by adding command-line options. It alsoallows customization by setting environment variables _R_CHECK_*_:, as described in Section“Tools” in R Internals: a set of these customizations similar to those used by CRAN can beselected by the option --as-cran (which works best if Internet access is available30).

You do need to ensure that the package is checked in a suitable locale if it contains non-ASCII

characters. Such packages are likely to fail some of the checks in a C locale, and R CMD check

will warn if it spots the problem. You should be able to check any package in a UTF-8 locale(if one is available). Beware that although a C locale is rarely used at a console, it may be thedefault if logging in remotely or for batch jobs.

Multiple sub-architectures: On systems which support multiple sub-architectures(principally Windows), R CMD check will install and check a package which con-tains compiled code under all available sub-architectures. (Use option --force-

multiarch to force this for packages without compiled code, which are otherwise

29 or if option --use-valgrind is used or environment variable _R_CHECK_ALWAYS_LOG_VIGNETTE_OUTPUT_ is setto a true value or if there are differences from a target output file

30 Windows users behind proxies may want to set environment variable R_WIN_INTERNET2 to a non-empty value,e.g. in ~/.R/check_environ. Some Windows users may need to set R_WIN_NO_JUNCTIONS to a non-emptyvalue.

Chapter 1: Creating R packages 29

only checked under the main sub-architecture.) This will run the loading tests, ex-amples and tests directory under each installed sub-architecture in turn, and givean error if any fail. Where environment variables (including perhaps PATH) need tobe set differently for each sub-architecture, these can be set in architecture-specificfiles such as R_HOME/etc/i386/Renviron.site.

An alternative approach is to use R CMD check --no-multiarch to check the pri-mary sub-architecture, and then to use something like R --arch=x86_64 CMD check

--extra-arch or (Windows) /path/to/R/bin/x64/Rcmd check --extra-arch torun for each additional sub-architecture just the checks31 which differ by sub-architecture. (This approach is required for packages which are installed by R CMD

INSTALL --merge-multiarch.)

Where packages need additional commands to install all the sub-architectures thesecan be supplied by e.g. --install-args=--force-biarch.

1.3.2 Building package tarballs

Packages may be distributed in source form as “tarballs” (.tar.gz files) or in binary form.The source form can be installed on all platforms with suitable tools and is the usual form forUnix-like systems; the binary form is platform-specific, and is the more common distributionform for the Windows and OS X platforms.

Using R CMD build, the R package builder, one can build R package tarballs from their sources(for example, for subsequent release).

Prior to actually building the package in the standard gzipped tar file format, a few diagnosticchecks and cleanups are performed. In particular, it is tested whether object indices exist andcan be assumed to be up-to-date, and C, C++ and FORTRAN source files and relevant makefilesin a src directory are tested and converted to LF line-endings if necessary.

Run-time checks whether the package works correctly should be performed using R CMD check

prior to invoking the final build procedure.

To exclude files from being put into the package, one can specify a list of exclude patternsin file .Rbuildignore in the top-level source directory. These patterns should be Perl-likeregular expressions (see the help for regexp in R for the precise details), one per line, to bematched32 against the file and directory names relative to the top-level package source directory.In addition, directories from source control systems33 or from eclipse34, directories with namesending .Rcheck or Old or old and files GNUMakefile, Read-and-delete-me or with base namesstarting with ‘.#’, or starting and ending with ‘#’, or ending in ‘~’, ‘.bak’ or ‘.swp’, are excludedby default. In addition, those files in the R, demo and man directories which are flagged by R CMD

check as having invalid names will be excluded.

Use R CMD build --help to obtain more information about the usage of the R packagebuilder.

Unless R CMD build is invoked with the --no-build-vignettes option (or the package’sDESCRIPTION contains ‘BuildVignettes: no’ or similar), it will attempt to (re)build the vi-gnettes (see Section 1.4 [Writing package vignettes], page 31) in the package. To do so it installsthe current package into a temporary library tree, but any dependent packages need to beinstalled in an available library tree (see the Note: at the top of this section).

Similarly, if the .Rd documentation files contain any \Sexpr macros (see Section 2.12 [Dy-namic pages], page 63), the package will be temporarily installed to execute them. Post-execution

31 loading, examples, tests, vignettes32 case-insensitively on Windows.33 called CVS or .svn or .arch-ids or .bzr or .git (but not files called .git) or .hg.34 called .metadata.

Chapter 1: Creating R packages 30

binary copies of those pages containing build-time macros will be saved in build/partial.rdb.If there are any install-time or render-time macros, a .pdf version of the package manual willbe built and installed in the build subdirectory. (This allows CRAN or other repositories todisplay the manual even if they are unable to install the package.) This can be suppressed bythe option --no-manual or if package’s DESCRIPTION contains ‘BuildManual: no’ or similar.

One of the checks that R CMD build runs is for empty source directories. These are inmost (but not all) cases unintentional, if they are intentional use the option --keep-empty-

dirs (or set the environment variable _R_BUILD_KEEP_EMPTY_DIRS_ to ‘TRUE’, or have a‘BuildKeepEmpty’ field with a true value in the DESCRIPTION file).

The --resave-data option allows saved images (.rda and .RData files) in the data directoryto be optimized for size. It will also compress tabular files and convert .R files to saved images.It can take values no, gzip (the default if this option is not supplied, which can be changedby setting the environment variable _R_BUILD_RESAVE_DATA_) and best (equivalent to giving itwithout a value), which chooses the most effective compression. Using best adds a dependenceon R (>= 2.10) to the DESCRIPTION file if bzip2 or xz compression is selected for any of thefiles. If this is thought undesirable, --resave-data=gzip (which is the default if that option isnot supplied) will do what compression it can with gzip. A package can control how its datais resaved by supplying a ‘BuildResaveData’ field (with one of the values given earlier in thisparagraph) in its DESCRIPTION file.

The --compact-vignettes option will run tools::compactPDF over the PDF files ininst/doc (and its subdirectories) to losslessly compress them. This is not enabled by default(it can be selected by environment variable _R_BUILD_COMPACT_VIGNETTES_) and needs qpdf

(http://qpdf.sourceforge.net/) to be available.

It can be useful to run R CMD check --check-subdirs=yes on the built tarball as a finalcheck on the contents.

Where a non-POSIX file system is in use which does not utilize execute permissions, somecare is needed with permissions. This applies on Windows and to e.g. FAT-formatted drives andSMB-mounted file systems on other OSes. The ‘mode’ of the file recorded in the tarball will bewhatever file.info() returns. On Windows this will record only directories as having executepermission and on other OSes it is likely that all files have reported ‘mode’ 0777. A particularissue is packages being built on Windows which are intended to contain executable scripts such asconfigure and cleanup: R CMD build ensures those two are recorded with execute permission.

Directory build of the package sources is reserved for use by R CMD build: it contains infor-mation which may not easily be created when the package is installed.

1.3.3 Building binary packages

Binary packages are compressed copies of installed versions of packages. They contain compiledshared libraries rather than C, C++ or Fortran source code, and the R functions are includedin their installed form. The format and filename are platform-specific; for example, a binarypackage for Windows is usually supplied as a .zip file, and for the OS X platform the defaultbinary package file extension is .tgz.

The recommended method of building binary packages is to use

R CMD INSTALL --build pkg where pkg is either the name of a source tarball (in the usual.tar.gz format) or the location of the directory of the package source to be built. This operatesby first installing the package and then packing the installed binaries into the appropriate binarypackage file for the particular platform.

By default, R CMD INSTALL --build will attempt to install the package into the default librarytree for the local installation of R. This has two implications:

• If the installation is successful, it will overwrite any existing installation of the same package.

Chapter 1: Creating R packages 31

• The default library tree must have write permission; if not, the package will not install andthe binary will not be created.

To prevent changes to the present working installation or to provide an install location withwrite access, create a suitably located directory with write access and use the -l option to buildthe package in the chosen location. The usage is then

R CMD INSTALL -l location --build pkg

where location is the chosen directory with write access. The package will be installed as asubdirectory of location, and the package binary will be created in the current directory.

Other options for R CMD INSTALL can be found using R CMD INSTALL --help, and platform-specific details for special cases (e.g. handling Fortran sources on OS X) are discussed in theplatform-specific FAQs.

In much earlier versions of R, R CMD build --binary could build a binary version of a package,but this approach is now deprecated in favour of R CMD INSTALL --build.

Finally, at least one web-based service is available for building binary packages from (checked)source code: WinBuilder (see http://win-builder.R-project.org/) is able to build Windowsbinaries. Note that this is intended for developers on other platforms who do not have access toWindows but wish to provide binaries for the Windows platform.

1.4 Writing package vignettes

In addition to the help files in Rd format, R packages allow the inclusion of documents inarbitrary other formats. The standard location for these is subdirectory inst/doc of a sourcepackage, the contents will be copied to subdirectory doc when the package is installed. Pointersfrom package help indices to the installed documents are automatically created. Documentsin inst/doc can be in arbitrary format, however we strongly recommend providing them inPDF format, so users on almost all platforms can easily read them. To ensure that they can beaccessed from a browser (as an HTML index is provided), the file names should start with anASCII letter and be comprised entirely of ASCII letters or digits or hyphen or underscore.

A special case is PDF documents with sources in Sweave, which we call package vignettes.(Since R 3.0.0, other vignette formats are supported; see Section 1.4.2 [Non-Sweave vignettes],page 33.) The preferred location for the sources is the subdirectory vignettes of the sourcepackage, but pro tem35 for compatibility with the layout before R 2.14.0, vignette sources willbe looked for in inst/doc if subdirectory vignettes does not exist. Note that the location ofthe vignette sources only affects R CMD build and R CMD check: the tarball built by R CMD build

includes in inst/doc the components36 intended to be installed.

Vignette sources are normally given the file extension .Rnw or .Rtex, but for historical reasonsextensions37 .Snw and .Stex are also recognized as vignettes. Sweave allows the integration ofLATEX documents: see the Sweave help page in R and the Sweave vignette in package utils fordetails on the document format. Package vignettes are tested by R CMD check by executing allR code chunks they contain (except those with option eval=FALSE). The R working directoryfor all vignette tests in R CMD check is a copy of the vignette source directory. Make sure allfiles needed to run the R code in the vignette (data sets, . . . ) are accessible by either placingthem in the inst/doc hierarchy of the source package or by using calls to system.file(). Allother files needed to re-make the vignette PDFs (such as LATEX style files, BiBTeX input filesand files for any figures not created by running the code in the vignette) must be in the vignettesource directory.

35 This is expected to be removed in R 3.1.0.36 at least the PDF documents and the Sweave files so these can be tangled to produce the R code.37 and to avoid problems with case-insensitive file systems, lower-case versions of all these extensions.

Chapter 1: Creating R packages 32

R CMD build will automatically38 create the (PDF or HTML versions of the) vignettes ininst/doc for distribution with the package sources. By including the vignettes in the packagesources it is not necessary that these can be re-built at install time, i.e., the package author canuse private R packages, screen snapshots and LATEX extensions which are only available on hismachine.39

By default R CMD build will run Sweave on all Sweave vignette source files in vignettes, orpro tem if that does not exist, inst/doc (but not in sub-directories). If no Makefile is found indirectory inst/doc, then tools::texi2pdf is run on all processed vignette sources. Whenevera Makefile is found in the vignette source directory, then R CMD build will try to run make

after the Sweave runs. The first target in the Makefile should take care of both creation ofPDF/HTML files and cleaning up afterwards (including after Sweave), i.e., delete all files thatshall not appear in the final package archive. Note that if the make step runs R it needs to becareful to respect the environment values of R_LIBS and R_HOME40. Finally, if there is a Makefileand it has a ‘clean:’ target, make clean is run.

All the usual caveats about including a Makefile apply. It must be portable (no GNU

extensions), use LF line endings and must work correctly with a parallel make: too many authorshave written things like

## BAD EXAMPLE

all: pdf clean

pdf: ABC-intro.pdf ABC-details.pdf

%.pdf: %.tex

texi2dvi --pdf $*

clean:

rm *.tex ABC-details-*.pdf

which will start removing the source files whilst pdflatex is working.

Note that it is pointless (and potentially misleading since the files might be outdated) toinclude in inst/doc R code files which would be generated from vignette sources, as these willbe re-generated when the package is built (unless the vignette does not generate any R code, inwhich case it is also pointless/misleading).

Metadata lines can be placed in the source file, preferably in LATEX comments in the preamble.One such is a \VignetteIndexEntry of the form

%\VignetteIndexEntry{Using Animal}

Others you may see are \VignettePackage (currently ignored), \VignetteDepends

and \VignetteKeyword (which replaced \VignetteKeywords). These are processed atpackage installation time to create the saved data frame Meta/vignette.rds, but onlythe \VignetteIndexEntry and \VignetteKeyword statements are currently used. The\VignetteEngine statement is described in Section 1.4.2 [Non-Sweave vignettes], page 33.

At install time an HTML index for all vignettes in the package is automatically cre-ated from the \VignetteIndexEntry statements unless a file index.html exists in directoryinst/doc. This index is linked from the HTML help index for the package. If you do supply ainst/doc/index.html file it should contain relative links only to files under the installed doc

directory, or perhaps (not really an index) to HTML help files or to the DESCRIPTION file.

38 unless inhibited by using ‘BuildVignettes: no’ in the DESCRIPTION file.39 provided the conditions of the package’s license are met: many, including CRAN, see the omission of source

components as incompatible with an Open Source license.40 R_HOME/bin is prepended to the PATH so that references to R or Rscript in the Makefile do make use of the

currently running version of R.

Chapter 1: Creating R packages 33

Sweave/Stangle allows the document to specify the split=TRUE option to create a single Rfile for each code chunk: this will not work for vignettes where it is assumed that each vignettesource generates a single file with the vignette extension replaced by .R.

Do watch that PDFs are not too large – one in a CRAN package was 72MB! This is usuallycaused by the inclusion of overly detailed figures, which will not render well in PDF viewers.Sometimes it is much better to generate fairly high resolution bitmap (PNG, JPEG) figures andinclude those in the PDF document.

When R CMD build builds the vignettes, it copies these and the vignette sources from direc-tory vignettes to inst/doc. To install any other files from the vignettes directory, includea file vignettes/.install_extras which specifies these as Perl-like regular expressions on oneor more lines. (See the description of the .Rinstignore file for full details.)

1.4.1 Encodings and vignettes

Vignettes will in general include descriptive text, R input, R output and figures, LATEX in-clude files and bibliographic references. As any of these may contain non-ASCII characters, thehandling of encodings can become very complicated.

The vignette source file should be written in ASCII or contain a declaration of the encoding(see below). This applies even to comments within the source file, since vignette engines processcomments to look for options and metadata lines. When an engine’s weave and tangle functionsare called on the vignette source, it will be converted to the encoding of the current R session.

Stangle() will produce an R code file in the current locale’s encoding: for a non-ASCII

vignette what that is recorded in a comment at the top of the file.

Sweave() will produce a .tex file in the current locale’s encoding. That needs to be declaredto LATEX via a line like

\usepackage[utf8]{inputenc}

(It is also possible to use the more recent ‘inputenx’ LATEX package.) R CMD check will warnabout any non-ASCII vignettes it finds which do not have such a declaration.

Sweave() will also parse and evaluate the R code in each chunk. The R output will alsobe in the current locale, and should be covered by the ‘inputenc’ declaration. One thingpeople often forget is that the R output may not be ASCII even for ASCII R sources, for manypossible reasons. One common one is the use of ‘fancy’ quotes: see the R help on sQuote:note carefully that it is not portable to declare UTF-8 or CP1252 to cover such quotes, as theirencoding will depend on the locale used to run Sweave(): this can be circumvented by settingoptions(useFancyQuotes="UTF-8") in the vignette.

The final issue is the encoding of figures – this applies only to PDF figures and not PNGetc. The PDF figures will contain declarations for their encoding, but the Sweave optionpdf.encoding may need to be set appropriately: see the help for the pdf() graphics device.

As a real example of the complexities, consider the fortunes package version ‘1.4-0’. Thatpackage did not have a declared encoding, and its vignette was in ASCII. However, the data itdisplays are read from a UTF-8 CSV file and will be assumed to be in the current encoding, sofortunes.tex will be in UTF-8 in any locale. Had read.table been told the data were UTF-8,fortunes.tex would have been in the locale’s encoding.

1.4.2 Non-Sweave vignettes

R 3.0.0 and later allow vignettes in formats other than Sweave by means of “vignette engines”.For example knitr version 1.1 or later can create .tex files from a variation on Sweave format,and .html files from a variation on “markdown” format. These engines replace the Sweave()

function with other functions to convert vignette source files into LATEX files for processing into.pdf, or directly into .pdf or .html files. The Stangle() function is replaced with a functionthat extracts the R source from a vignette.

Chapter 1: Creating R packages 34

R recognizes non-Sweave vignettes using filename extensions specified by the engine. Forexample, the knitr package supports the extension .Rmd (standing for “R markdown”). Theuser indicates the vignette engine within the vignette source using a \VignetteEngine line, forexample

%\VignetteEngine{knitr::knitr}

This specifies the name of a package and an engine to use in place of Sweave in processing thevignette. As Sweave is the only engine supplied with the R distribution, the package providingany other engine must be specified in the ‘VignetteBuilder’ field of the package DESCRIPTIONfile, and also specified in the ‘Suggests’, ‘Imports’ or ‘Depends’ field (since its namespace mustbe available to build or check your package). If more than one package is specified as a builder,they will be searched in the order given there. The utils package is always implicitly appendedto the list of builder packages, but may be included earlier to change the search order.

Note that a package with non-Sweave vignettes should always have a ‘VignetteBuilder’field in the DESCRIPTION file, since this is how R CMD check recognizes that there are vignettesto be checked.

The vignette engine can produce .tex, .pdf, or .html files as output. If it produces .texfiles, R will call pdflatex to convert them to .pdf for display to the user.

Package writers who would like to supply vignette engines need to register those engines inthe package .onLoad function. For example, that function could make the call

tools::vignetteEngine("knitr", weave = vweave, tangle = vtangle,

pattern = "[.]Rmd$", package = "knitr")

(The actual registration in knitr is more complicated, because it supports other input formats.)See the ?tools::vignetteEngine help topic for details on engine registration.

1.5 Package namespaces

R has a namespace management system for code in packages. This system allows the packagewriter to specify which variables in the package should be exported to make them available topackage users, and which variables should be imported from other packages.

The mechanism for specifying a namespace for a package is to place a NAMESPACE file in thetop level package directory. This file contains namespace directives describing the imports andexports of the namespace. Additional directives register any shared objects to be loaded andany S3-style methods that are provided. Note that although the file looks like R code (and oftenhas R-style comments) it is not processed as R code. Only very simple conditional processingof if statements is implemented.

Packages are loaded and attached to the search path by calling library or require. Only theexported variables are placed in the attached frame. Loading a package that imports variablesfrom other packages will cause these other packages to be loaded as well (unless they havealready been loaded), but they will not be placed on the search path by these implicit loads.

Namespaces are sealed once they are loaded. Sealing means that imports and exports cannotbe changed and that internal variable bindings cannot be changed. Sealing allows a simplerimplementation strategy for the namespace mechanism. Sealing also allows code analysis andcompilation tools to accurately identify the definition corresponding to a global variable referencein a function body.

The namespace controls the search strategy for variables used by functions in the package.If not found locally, R searches the package namespace first, then the imports, then the basenamespace and then the normal search path.

Prior to R 2.14.0, namespaces were optional in packages: a default namespace was generatedon installation in 2.14.x and 2.15.x. As from 3.0.0 a namespace is mandatory.

Chapter 1: Creating R packages 35

1.5.1 Specifying imports and exports

Exports are specified using the export directive in the NAMESPACE file. A directive of the form

export(f, g)

specifies that the variables f and g are to be exported. (Note that variable names may bequoted, and reserved words and non-standard names such as [<-.fractions must be.)

For packages with many variables to export it may be more convenient to specify the namesto export with a regular expression using exportPattern. The directive

exportPattern("^[^\\.]")

exports all variables that do not start with a period. However, such broad patterns are notrecommended for production code: it is better to list all exports or use narrowly-definedgroups. (This pattern applies to S4 classes.) Beware of patterns which include names start-ing with a period: some of these are internal-only variables and should never be exported, e.g.‘.__S3MethodsTable__.’ (and the code nowadays excludes known cases).

Packages implicitly import the base namespace. Variables exported from other packageswith namespaces need to be imported explicitly using the directives import and importFrom.The import directive imports all exported variables from the specified package(s). Thus thedirectives

import(foo, bar)

specifies that all exported variables in the packages foo and bar are to be imported. If onlysome of the exported variables from a package are needed, then they can be imported usingimportFrom. The directive

importFrom(foo, f, g)

specifies that the exported variables f and g of the package foo are to be imported. UsingimportFrom selectively rather than import is good practice.

It is possible to export variables from a namespace which it has imported from other name-spaces: this has to be done explicitly and not via exportPattern.

If a package only needs a few objects from another package it can use a fully qualified variablereference in the code instead of a formal import. A fully qualified reference to the function f inpackage foo is of the form foo::f. This is slightly less efficient than a formal import and alsoloses the advantage of recording all dependencies in the NAMESPACE file (but they still need to berecorded in the DESCRIPTION file). Evaluating foo::f will cause package foo to be loaded, butnot attached, if it was not loaded already—this can be an advantage in delaying the loading ofa rarely used package.

Using foo:::f instead of foo::f allows access to unexported objects. This is generally notrecommended, as the semantics of unexported objects may be changed by the package authorin routine maintenance.

1.5.2 Registering S3 methods

The standard method for S3-style UseMethod dispatching might fail to locate methods definedin a package that is imported but not attached to the search path. To ensure that these methodsare available the packages defining the methods should ensure that the generics are importedand register the methods using S3method directives. If a package defines a function print.foo

intended to be used as a print method for class foo, then the directive

S3method(print, foo)

ensures that the method is registered and available for UseMethod dispatch, and the functionprint.foo does not need to be exported. Since the generic print is defined in base it does notneed to be imported explicitly.

Chapter 1: Creating R packages 36

(Note that function and class names may be quoted, and reserved words and non-standardnames such as [<- and function must be.)

It is possible to specify a third argument to S3method, the function to be used as the method,for example

S3method(print, check_so_symbols, .print.via.format)

when print.check_so_symbols is not needed.

There used to be a limit on the number of S3method directives: it was 500 prior to R 3.0.2.

1.5.3 Load hooks

There are a number of hooks called as packages are loaded, attached, detached, and unloaded.See help(".onLoad") for more details.

Since loading and attaching are distinct operations, separate hooks are provided for each.These hook functions are called .onLoad and .onAttach. They both take arguments41 libnameand pkgname; they should be defined in the namespace but not exported.

Packages can use a .onDetach (as from R 3.0.0) or .Last.lib function (provided the lat-ter is exported from the namespace) when detach is called on the package. It is called witha single argument, the full path to the installed package. There is also a hook .onUnload

which is called when the namespace is unloaded (via a call to unloadNamespace, perhaps calledby detach(unload = TRUE)) with argument the full path to the installed package’s directory..onUnload and .onDetach should be defined in the namespace and not exported, but .Last.libdoes need to be exported.

Packages are not likely to need .onAttach (except perhaps for a start-up banner); code toset options and load shared objects should be placed in a .onLoad function, or use made of theuseDynLib directive described next.

User-level hooks are also available: see the help on function setHook.

These hooks are often used incorrectly. People forget to export .Last.lib. Compiledcode should be loaded in .onLoad (or via a useDynLb directive: see below) and unloaded in.onUnload. Do remember that a package’s namespace can be loaded without the namespacebeing attached (e.g. by pkgname::fun) and that a package can be detached and re-attachedwhilst its namespace remains loaded.

1.5.4 useDynLib

A NAMESPACE file can contain one or more useDynLib directives which allows shared objects thatneed to be loaded.42 The directive

useDynLib(foo)

registers the shared object foo43 for loading with library.dynam. Loading of registered ob-ject(s) occurs after the package code has been loaded and before running the load hook func-tion. Packages that would only need a load hook function to load a shared object can use theuseDynLib directive instead.

The useDynLib directive also accepts the names of the native routines that are to be used inR via the .C, .Call, .Fortran and .External interface functions. These are given as additionalarguments to the directive, for example,

useDynLib(foo, myRoutine, myOtherRoutine)

41 they will be called with two unnamed arguments, in that order.42 NB: this will only be read in all versions of R if the package contains R code in a R directory.43 Note that this is the basename of the shared object, and the appropriate extension (.so or .dll) will be

added.

Chapter 1: Creating R packages 37

By specifying these names in the useDynLib directive, the native symbols are resolved whenthe package is loaded and R variables identifying these symbols are added to the package’snamespace with these names. These can be used in the .C, .Call, .Fortran and .External

calls in place of the name of the routine and the PACKAGE argument. For instance, we can callthe routine myRoutine from R with the code

.Call(myRoutine, x, y)

rather than

.Call("myRoutine", x, y, PACKAGE = "foo")

There are at least two benefits to this approach. Firstly, the symbol lookup is done justonce for each symbol rather than each time the routine is invoked. Secondly, this removes anyambiguity in resolving symbols that might be present in several compiled DLLs.

In some circumstances, there will already be an R variable in the package with the same nameas a native symbol. For example, we may have an R function in the package named myRoutine.In this case, it is necessary to map the native symbol to a different R variable name. This canbe done in the useDynLib directive by using named arguments. For instance, to map the nativesymbol name myRoutine to the R variable myRoutine_sym, we would use

useDynLib(foo, myRoutine_sym = myRoutine, myOtherRoutine)

We could then call that routine from R using the command

.Call(myRoutine_sym, x, y)

Symbols without explicit names are assigned to the R variable with that name.

In some cases, it may be preferable not to create R variables in the package’s namespacethat identify the native routines. It may be too costly to compute these for many routineswhen the package is loaded if many of these routines are not likely to be used. In this case,one can still perform the symbol resolution correctly using the DLL, but do this each time theroutine is called. Given a reference to the DLL as an R variable, say dll, we can call the routinemyRoutine using the expression

.Call(dll$myRoutine, x, y)

The $ operator resolves the routine with the given name in the DLL using a call togetNativeSymbol. This is the same computation as above where we resolve the symbol when thepackage is loaded. The only difference is that this is done each time in the case of dll$myRoutine.

In order to use this dynamic approach (e.g., dll$myRoutine), one needs the reference to theDLL as an R variable in the package. The DLL can be assigned to a variable by using thevariable = dllName format used above for mapping symbols to R variables. For example, ifwe wanted to assign the DLL reference for the DLL foo in the example above to the variablemyDLL, we would use the following directive in the NAMESPACE file:

myDLL = useDynLib(foo, myRoutine_sym = myRoutine, myOtherRoutine)

Then, the R variable myDLL is in the package’s namespace and available for calls such asmyDLL$dynRoutine to access routines that are not explicitly resolved at load time.

If the package has registration information (see Section 5.4 [Registering native routines],page 88), then we can use that directly rather than specifying the list of symbols again inthe useDynLib directive in the NAMESPACE file. Each routine in the registration informationis specified by giving a name by which the routine is to be specified along with the addressof the routine and any information about the number and type of the parameters. Using the.registration argument of useDynLib, we can instruct the namespace mechanism to create Rvariables for these symbols. For example, suppose we have the following registration informationfor a DLL named myDLL:

R_CMethodDef cMethods[] = {

{"foo", (DL_FUNC) &foo, 4, {REALSXP, INTSXP, STRSXP, LGLSXP}},

Chapter 1: Creating R packages 38

{"bar_sym", (DL_FUNC) &bar, 0},

{NULL, NULL, 0}

};

R_CallMethodDef callMethods[] = {

{"R_call_sym", (DL_FUNC) &R_call, 4},

{"R_version_sym", (DL_FUNC) &R_version, 0},

{NULL, NULL, 0}

};

Then, the directive in the NAMESPACE file

useDynLib(myDLL, .registration = TRUE)

causes the DLL to be loaded and also for the R variables foo, bar_sym, R_call_sym and R_

version_sym to be defined in the package’s namespace.

Note that the names for the R variables are taken from the entry in the registration informa-tion and do not need to be the same as the name of the native routine. This allows the creatorof the registration information to map the native symbols to non-conflicting variable names inR, e.g. R_version to R_version_sym for use in an R function such as

R_version <- function()

{

.Call(R_version_sym)

}

Using argument .fixes allows an automatic prefix to be added to the registered symbols,which can be useful when working with an existing package. For example, package KernSmoothhas

useDynLib(KernSmooth, .registration = TRUE, .fixes = "F_")

which makes the R variables corresponding to the FORTRAN symbols F_bkde and so on, andso avoid clashes with R code in the namespace.

1.5.5 An example

As an example consider two packages named foo and bar. The R code for package foo in filefoo.R is� �

x <- 1

f <- function(y) c(x,y)

foo <- function(x) .Call("foo", x, PACKAGE="foo")

print.foo <- function(x, ...) cat("<a foo>\n") Some C code defines a C function compiled into DLL foo (with an appropriate extension). TheNAMESPACE file for this package is� �

useDynLib(foo)

export(f, foo)

S3method(print, foo) The second package bar has code file bar.R� �

c <- function(...) sum(...)

g <- function(y) f(c(y, 7))

h <- function(y) y+9

Chapter 1: Creating R packages 39

and NAMESPACE file� �import(foo)

export(g, h) Calling library(bar) loads bar and attaches its exports to the search path. Package foo is alsoloaded but not attached to the search path. A call to g produces

> g(6)

[1] 1 13

This is consistent with the definitions of c in the two settings: in bar the function c is definedto be equivalent to sum, but in foo the variable c refers to the standard function c in base.

1.5.6 Namespaces with S4 classes and methods

Some additional steps are needed for packages which make use of formal (S4-style) classes andmethods (unless these are purely used internally). The package should have Depends: methods

in its DESCRIPTION file44 and import(methods) or importFrom(methods, ...) plus any classesand methods which are to be exported need to be declared in the NAMESPACE file. For example,the stats4 package has

export(mle) # exporting methods implicitly exports the generic

importFrom("graphics", plot)

importFrom("stats", optim, qchisq)

## For these, we define methods or (AIC, BIC, nobs) an implicit generic:

importFrom("stats", AIC, BIC, coef, confint, logLik, nobs, profile,

update, vcov)

exportClasses(mle, profile.mle, summary.mle)

## All methods for imported generics:

exportMethods(coef, confint, logLik, plot, profile, summary,

show, update, vcov)

## implicit generics which do not have any methods here

export(AIC, BIC, nobs)

All S4 classes to be used outside the package need to be listed in an exportClasses direc-tive. Alternatively, they can be specified using exportClassPattern45 in the same style asfor exportPattern. To export methods for generics from other packages an exportMethods

directive can be used.

Note that exporting methods on a generic in the namespace will also export the generic, andexporting a generic in the namespace will also export its methods. If the generic function is notlocal to this package, either because it was imported as a generic function or because the non-generic version has been made generic solely to add S4 methods to it (as for functions such asplot in the example above), it can be declared via either or both of export or exportMethods,but the latter is clearer (and is used in the stats4 example above). In particular, for primitivefunctions there is no generic function, so export would export the primitive, which makes nosense. On the other hand, if the generic is local to this package, it is more natural to export thefunction itself using export(), and this must be done if an implicit generic is created withoutsetting any methods for it (as is the case for AIC in stats4).

A non-local generic function is only exported to ensure that calls to the function will dispatchthe methods from this package (and that is not done or required when the methods are for

44 This was necessary at least prior to R 3.0.2 as the methods package looked for its own R code on the searchpath.

45 This defaults to the same pattern as exportPattern: use something like exportClassPattern("^$") tooverride this.

Chapter 1: Creating R packages 40

primitive functions). For this reason, you do not need to document such implicitly createdgeneric functions, and undoc in package tools will not report them.

If a package uses S4 classes and methods exported from another package, but does not importthe entire namespace of the other package, it needs to import the classes and methods explicitly,with directives

importClassesFrom(package, ...)

importMethodsFrom(package, ...)

listing the classes and functions with methods respectively. Suppose we had two small packagesA and B with B using A. Then they could have NAMESPACE files� �

export(f1, ng1)

exportMethods("[")

exportClasses(c1) and � �

importFrom(A, ng1)

importClassesFrom(A, c1)

importMethodsFrom(A, f1)

export(f4, f5)

exportMethods(f6, "[")

exportClasses(c1, c2) respectively.

Note that importMethodsFrom will also import any generics defined in the namespace onthose methods.

It is important if you export S4 methods that the corresponding generics are available. Youmay for example need to import plot from graphics to make visible a function to be convertedinto its implicit generic. But it is better practice to make use of the generics exported by stats4as this enables multiple packages to unambiguously set methods on those generics.

1.6 Writing portable packages

This section contains advice on writing packages to be used on multiple platforms or for distri-bution (for example to be submitted to a package repository such as CRAN).

Portable packages should have simple file names: use only alphanumeric ASCII charactersand ., and avoid those names not allowed under Windows which are mentioned above.

Many of the graphics devices are platform-specific: even X11() (aka x11()) which althoughemulated on Windows may not be available on a Unix-alike (and is not the preferred screendevice on OS X). It is rarely necessary for package code or examples to open a new device, butif essential, use dev.new().

Use R CMD build to make the release .tar.gz file.

R CMD check provides a basic set of checks, but often further problems emerge when peopletry to install and use packages submitted to CRAN – many of these involve compiled code. Hereare some further checks that you can do to make your package more portable.

• If your package has a configure script, provide a configure.win script to be used onWindows (an empty file if no actions are needed).

• If your package has a Makevars or Makefile file, make sure that you use only portablemake features. Such files should be LF-terminated (including the final line of the file) and

Chapter 1: Creating R packages 41

not make use of GNU extensions. Commonly misused GNU extensions are conditionalinclusions (ifeq and the like), ${shell ...} and ${wildcard ...}, and the use of += and:=. Also, the use of $< other than in implicit rules is a GNU extension, as is the $^ macroUnfortunately makefiles which use GNU extensions often run on other platforms but do nothave the intended results.

The use of ${shell ...} can be avoided by using backticks, e.g.

PKG_CPPFLAGS = ‘gsl-config --cflags‘

which works in all versions of make known46 to be used with R.

If you really must assume GNU make, declare it in the DESCRIPTION file by

SystemRequirements: GNU make

Since the only viable make for Windows is GNU make, it is permissible to use GNU exten-sions in files Makevars.win or Makefile.win.

Moreover, Bash extensions also need to be avoided in shell scripts, including expres-sion in Makefiles (which are passed to the shell for processing). Some R platforms usestrictly POSIX-conformant Bourne shells, and Windows and some Unix-alike OSes use ash(http://en.wikipedia.org/wiki/Almquist_shell), a rather minimal shell with fewbuiltins. Beware of assuming that all the POSIX command-line utilities are available, espe-cially on Windows where only a minimal set is provided for use with R. (See Section “Thecommand line tools” in R Installation and Administration.) One particular issue is the useof echo, for which two behaviours are allowed (http://pubs.opengroup.org/onlinepubs/9699919799/utilities/echo.html) and both occur as defaults on R platforms: portableapplications should not use -n (as the first argument) or escape sequences.

• Make use of the abilities of your compilers to check the standards-conformance of yourcode. For example, gcc can be used with options -Wall -pedantic to alert you to potentialproblems. This is particularly important for C++, where g++ -Wall -pedantic will alertyou to the use of GNU extensions which fail to compile on most other C++ compilers. IfR was not configured accordingly, one can achieve this via personal Makevars files. SeeSection “Customizing package compilation” in R Installation and Administration,

Although there is a 2011 version of the C++ standard, it is not yet fully implemented (noris it likely to be widely available for some years) and portable C++ code needs to follow the1998 standard (and not use features from C99).

Similarly, the 2011 C standard is unlikely to be widely implemented for several years.

If you use FORTRAN 77, ftnchek (http://www.dsm.fordham.edu/~ftnchek/) providesthorough testing of conformance to the standard.

• Do be very careful with passing arguments between R, C and FORTRAN code. In particular,long in C will be 32-bit on some R platforms (including 64-bit Windows), but 64-bit onmost modern Unix and Linux platforms. It is rather unlikely that the use of long in C codehas been thought through: if you need a longer type than int you should use a configuretest for a C99 type such as int_fast64_t (and failing that, long long47) and typedef yourown type to be long or long long, or use another suitable type (such as size_t).

It is not safe to assume that long and pointer types are the same size, and they are not on64-bit Windows. If you need to convert pointers to and from integers use the C99 integertypes intptr_t and uintptr_t (which are defined in the header <stdint.h> and are notrequired to be implemented by the C99 standard).

Note that integer in FORTRAN corresponds to int in C on all R platforms.

46 GNU make, BSD make as in FreeBSD, AT&T make as implemented on Solaris.47 but note that long long is not a standard C++ type, and C++ compilers set up for strict checking will reject

it.

Chapter 1: Creating R packages 42

• Under no circumstances should your compiled code ever call abort or exit: these terminatethe user’s R process, quite possibly including all his unsaved work. One usage that could callabort is the assertmacro in C or C++ functions, which should never be active in productioncode. The normal way to ensure that is to define the macro NDEBUG, and R CMD INSTALL

does so as part of the compilation flags. If you wish to use assert during development. youcan include -UNDEBUG in PKG_CPPFLAGS. Note that your own src/Makefile or makefiles insub-directories may also need to define NDEBUG.

This applies not only to your own code but to any external software you compile in or linkto.

• Compiled code should not write to stdout or stderr and C++ and Fortran I/O should notbe used. As with the previous item such calls may come from external software and maynever be called, but package authors are often mistaken about that.

• Errors in memory allocation and reading/writing outside arrays are very common causes ofcrashes (e.g., segfaults) on some machines. See Section 4.3.2 [Using valgrind], page 78 for atool which can be used to look for this.

• Many platforms will allow unsatisfied entry points in compiled code, but will crash theapplication (here R) if they are ever used. Some (notably Windows) will not. Looking atthe output of

nm -pg mypkg.so # or other extension such as .sl

and checking if any of the symbols marked U is unexpected is a good way to avoid this.

• Conflicts between symbols in DLLs are handled in very platform-specific ways. Good waysto avoid trouble are to make as many symbols as possible static (check with nm -pg), andto use names which are clearly tied to your package (which also helps users if anything doesgo wrong).

• It is not portable to call compiled code in R or other packages via .Internal, .C, .Fortran,.Call or .External, since such interfaces are subject to change without notice and willprobably result in your code terminating the R process.

• Do not use (hard or symbolic) file links in your package sources. Where possible R CMD

build will replace them by copies.

• If you do not yourself have a Windows system, consider submitting your source package toWinBuilder (http://win-builder.r-project.org/) before distribution.

Do be careful in what your tests actually test. Bad practice seen in distributed packagesinclude:

• It is not reasonable to test the time taken by a command: you cannot know how fast orhow heavily loaded an R platform might be. At best you can test a ratio of times, and eventhat is fraught with difficulties.

• Do not test the exact format of R error messages: they change, and they can be translated.

• Only test the accuracy of results if you have done a formal error analysis. Things such aschecking that probabilities numerically sum to one are silly: numerical tests should alwayshave a tolerance. That the tests on your platform achieve a particular tolerance says littleabout other platforms. Most R platforms use ‘ix86’ or ‘x86_64’ CPUs: these use extendedprecision registers on some but not all of their FPU instructions. Thus the achieved precisioncan depend on the compiler version and optimization flags—our experience is that 32-bitbuilds tend to be less precise than 64-bit ones. But not all platforms use those CPUs, andnot all48 which use them configure them allow the use of extended precision.

48 Not doing so is the default on Windows, overridden for the R executables. It is also the default on someSolaris compilers.

Chapter 1: Creating R packages 43

If you must try to establish a tolerance empirically, configure and build R with --disable-

long-doubles and use appropriate compiler flags (such as -ffloat-store for gcc) tomitigate the effects of extended-precision calculations.

1.6.1 PDF size

There are a several tools available to reduce the size of PDF files: often the size can be reducedsubstantially with no or minimal loss in quality. Not only do large files take up space: they canstress the PDF viewer and take many minutes to print (if they can be printed at all).

qpdf (http://qpdf.sourceforge.net/) can compress losslessly. It is fairly readily available(e.g. it has binaries for Windows and packages in Debian/Ubuntu/Fedora 17, and is installedas part of the CRAN OS X distribution of R). R CMD build has an option to run qpdf overPDF files under inst/doc and replace them if at least 10Kb and 10% is saved. The full pathto the qpdf command can be supplied as environment variable R_QPDF (and is on the CRANbinary of R for OS X). It seems MiKTeX does not use PDF object compression and so qpdf canreduce considerably the files it outputs: MiKTeX can be overridden by code in the preamble ofan Sweave or LATEX file — see how this is done for the R reference manual at https://svn.r-project.org/R/trunk/doc/manual/refman.top.

Other tools can reduce the size of PDFs containing bitmap images at excessively high reso-lution. These are often best re-generated (for example Sweave defaults to 300 ppi, and 100–150is more appropriate for a package manual). These tools include Adobe Acrobat (not Reader),Apple’s Preview49 and Ghostscript (which converts PDF to PDF by

ps2pdf options -dAutoRotatePages=/None in.pdf out.pdf

and suitable options might be

-dPDFSETTINGS=/ebook

-dPDFSETTINGS=/screen

; see http://www.ghostscript.com/doc/9.07/Ps2pdf.htm for more such and consider allthe options for image downsampling). There have been examples in CRAN packages for whichGhostscript 9.06 and later produced dramatically better reductions than 9.05 or earlier.

We come across occasionally large PDF files containing excessively complicated figures usingPDF vector graphics: such figures are often best redesigned or failing that, output as PNG files.

Option --compact-vignettes to R CMD build defaults to value ‘qpdf’: use ‘both’ totry harder to reduce the size, provided you have Ghostscript available (see the help fortools::compactPDF).

1.6.2 Check timing

There are several ways to find out where time is being spent in the check process. Start by settingthe environment variable _R_CHECK_TIMINGS_ to ‘0’. This will report the total CPU times (notWindows) and elapsed times for installation and running examples, tests and vignettes, undereach sub-architecture if appropriate. For tests and vignettes, it reports the time for each as wellas the total.

Setting _R_CHECK_TIMINGS_ to a positive value sets a threshold (in seconds elapsed time) forreporting timings.

If you need to look in more detail at the timings for examples, use option --timings to R

CMD check (this is implied by --as-cran as from R 3.0.2). This adds a summary to the checkoutput for all the examples with CPU or elapsed time of more than 5 seconds. It produces a filemypkg.Rcheck/mypkg-Ex.timings containing timings for each help file: it is a tab-delimitedfile which can be read into R for further analysis.

49 Select ‘Save as’, and select ‘Reduce file size’ from the ‘Quartz filter’ menu’: this can be accessed in other ways,for example by Automator.

Chapter 1: Creating R packages 44

Timings for the tests and vignette runs are given at the bottom of the corresponding logfile: note that log files for successful vignette runs are only retained if environment variable_R_CHECK_ALWAYS_LOG_VIGNETTE_OUTPUT_ is set to a true value.

1.6.3 Encoding issues

Care is needed if your package contains non-ASCII text, and in particular if it is intended to beused in more than one locale. It is possible to mark the encoding used in the DESCRIPTION fileand in .Rd files, as discussed elsewhere in this manual.

First, consider carefully if you really need non-ASCII text. Many users of R will only beable to view correctly text in their native language group (e.g. Western European, EasternEuropean, Simplified Chinese) and ASCII.50. Other characters may not be rendered at all,rendered incorrectly, or cause your R code to give an error. For .Rd documentation, markingthe encoding and including ASCII transliterations is likely to do a reasonable job. The set ofcharacters which is commonly supported is wider than it used to be around 2000, but non-Latinalphabets (Greek, Russian, Georgian, . . . ) are still often problematic and those with double-width characters (Chinese, Japanese, Korean) often need specialist fonts to render correctly.

Several CRAN packages have messages in their R code in French (and a few in German). Abetter way to tackle this is to use the internationalization facilities discussed elsewhere in thismanual.

Function showNonASCIIfile in package tools can help in finding non-ASCII bytes in files.

There is a portable way to have arbitrary text in character strings (only) in your R code,which is to supply them in Unicode as \uxxxx escapes. If there are any characters not in thecurrent encoding the parser will encode the character string as UTF-8 and mark it as such.This applies also to character strings in datasets: they can be prepared using \uxxxx escapes orencoded in UTF-8 in a UTF-8 locale, or even converted to UTF-8 via ‘iconv()’. If you do this,make sure you have ‘R (>= 2.10)’ (or later) in the ‘Depends’ field of the DESCRIPTION file.

R sessions running in non-UTF-8 locales will if possible re-encode such strings for display(and this is done by RGui on Windows, for example). Suitable fonts will need to be selectedor made available51 both for the console/terminal and graphics devices such as ‘X11()’ and‘windows()’. Using ‘postscript’ or ‘pdf’ will choose a default 8-bit encoding depending on thelanguage of the UTF-8 locale, and your users would need to be told how to select the ‘encoding’argument.

If you want to run R CMD check on a Unix-alike over a package that sets a package encodingin its DESCRIPTION file you may need to specify a suitable locale via environment variable R_

ENCODING_LOCALES. The default is equivalent to the value

"latin1=en_US:latin2=pl_PL:UTF-8=en_US.UTF-8:latin9=fr_FR.iso885915@euro"

(which is appropriate for a system based on glibc) except that if the current locale is UTF-8then the package code is translated to UTF-8 for syntax checking.

1.6.4 Binary distribution

If you want to distribute a binary version of a package on Windows or OS X, there are furtherchecks you need to do to check it is portable: it is all too easy to depend on external softwareon your own machine that other users will not have.

For Windows, check what other DLLs your package’s DLL depends on (‘imports’ from in theDLL tools’ parlance). A convenient GUI-based tool to do so is ‘Dependency Walker’ (http://www.dependencywalker.com/) for both 32-bit and 64-bit DLLs – note that this will report

50 except perhaps some special characters such as backslash and hash which may be taken over for currencysymbols.

51 Typically on a Unix-alike this is done by telling fontconfig where to find suitable fonts to select glyphs from.

Chapter 1: Creating R packages 45

as missing links to R’s own DLLs such as R.dll and Rblas.dll. For 32-bit DLLs only, thecommand-line tool pedump.exe -i (in Rtools*.exe) can be used, and for the brave, the objdumptool in the appropriate toolchain will also reveal what DLLs are imported from. If you use atoolchain other than one provided by the R developers or use your own makefiles, watch out inparticular for dependencies on the toolchain’s runtime DLLs such as libgfortran, libstdc++and libgcc_s.

For OS X, using R CMD otool -L on the package’s shared objects in the libs directorywill show what they depend on: watch for any dependencies in /usr/local/lib, notablylibgfortran.2.dylib.

Many people (including the CRAN package repository) will not accept source packages con-taining binary files as the latter are a security risk. If you want to distribute a source packagewhich needs external software on Windows or OS X, options include

• To arrange for installation of the package to download the additional software from a URL,as e.g. package Cairo does.

• (For CRAN.) To negotiate with Uwe Ligges to host the additional components on Win-Builder, and write a configure.win file to install them. There used to be many examples,e.g. package rgdal (however nowadays CRAN prefers to use a uniform cross-compilationapproach for software such as GDAL).

Be aware that license requirements will need to be met so you may need to supply the sourcesfor the additional components (and will if your package has a GPL-like license).

1.7 Diagnostic messages

Diagnostic messages can be made available for translation, so it is important to write them ina consistent style. Using the tools described in the next section to extract all the messages cangive a useful overview of your consistency (or lack of it). Some guidelines follow.

• Messages are sentence fragments, and not viewed in isolation. So it is conventional not tocapitalize the first word and not to end with a period (or other punctuation).

• Try not to split up messages into small pieces. In C error messages use a single formatstring containing all English words in the messages.

In R error messages do not construct a message with paste (such messages will not betranslated) but via multiple arguments to stop or warning, or via gettextf.

• Do not use colloquialisms such as “can’t” and “don’t”.

• Conventionally single quotation marks are used for quotations such as

’ord’ must be a positive integer, at most the number of knots

and double quotation marks when referring to an R character string or a class, such as

’format’ must be "normal" or "short" - using "normal"

Since ASCII does not contain directional quotation marks, it is best to use ‘’’ and let thetranslator (including automatic translation) use directional quotations where available. Therange of quotation styles is immense: unfortunately we cannot reproduce them in a portabletexinfo document. But as a taster, some languages use ‘up’ and ‘down’ (comma) quotesrather than left or right quotes, and some use guillemets (and some use what Adobe calls‘guillemotleft’ to start and others use it to end).

In R messages it is also possible to use sQuote or dQuote as in

stop(gettextf("object must be of class %s or %s",

dQuote("manova"), dQuote("maov")),

domain = NA)

Chapter 1: Creating R packages 46

• Occasionally messages need to be singular or plural (and in other languages there may beno such concept or several plural forms – Slovenian has four). So avoid constructions suchas was once used in library

if((length(nopkgs) > 0) && !missing(lib.loc)) {

if(length(nopkgs) > 1)

warning("libraries ",

paste(sQuote(nopkgs), collapse = ", "),

" contain no packages")

else

warning("library ", paste(sQuote(nopkgs)),

" contains no package")

}

and was replaced by

if((length(nopkgs) > 0) && !missing(lib.loc)) {

pkglist <- paste(sQuote(nopkgs), collapse = ", ")

msg <- sprintf(ngettext(length(nopkgs),

"library %s contains no packages",

"libraries %s contain no packages",

domain = "R-base"),

pkglist)

warning(msg, domain=NA)

}

Note that it is much better to have complete clauses as here, since in another language onemight need to say ‘There is no package in library %s’ or ‘There are no packages in libraries%s’.

1.8 Internationalization

There are mechanisms to translate the R- and C-level error and warning messages. There areonly available if R is compiled with NLS support (which is requested by configure option--enable-nls, the default).

The procedures make use of msgfmt and xgettext which are part of GNU gettext and thiswill need to be installed: Windows users can find pre-compiled binaries at http://www.stats.ox.ac.uk/pub/Rtools/goodies/gettext-tools.zip.

1.8.1 C-level messages

The process of enabling translations is

• In a header file that will be included in all the C (or C++ or Objective C/C++) files containingmessages that should be translated, declare

#include <R.h> /* to include Rconfig.h */

#ifdef ENABLE_NLS

#include <libintl.h>

#define _(String) dgettext ("pkg", String)

/* replace pkg as appropriate */

#else

#define _(String) (String)

#endif

• For each message that should be translated, wrap it in _(...), for example

error(_("’ord’ must be a positive integer"));

Chapter 1: Creating R packages 47

If you want to use different messages for singular and plural forms, you need to add

#ifndef ENABLE_NLS

#define dngettext(pkg, String, StringP, N) (N > 1 ? StringP : String)

#endif

and mark strings by

dngettext(("pkg", <singular string>, <plural string>, n)

• In the package’s src directory run

xgettext --keyword=_ -o pkg.pot *.c

The file src/pkg.pot is the template file, and conventionally this is shipped as po/pkg.pot.

1.8.2 R messages

Mechanisms are also available to support the automatic translation of R stop, warning andmessage messages. They make use of message catalogs in the same way as C-level messages,but using domain R-pkg rather than pkg. Translation of character strings inside stop, warningand message calls is automatically enabled, as well as other messages enclosed in calls to gettextor gettextf. (To suppress this, use argument domain=NA.)

Tools to prepare the R-pkg.pot file are provided in package tools: xgettext2pot will preparea file from all strings occurring inside gettext/gettextf, stop, warning and message calls.Some of these are likely to be spurious and so the file is likely to need manual editing. xgettextextracts the actual calls and so is more useful when tidying up error messages.

The R function ngettext provides an interface to the C function of the same name: see exam-ple in the previous section. It is safest to use domain="R-pkg" explicitly in calls to ngettext,and necessary for earlier versions of R unless they are calls directly from a function in thepackage.

1.8.3 Preparing translations

Once the template files have been created, translations can be made. Conventional translationshave file extension .po and are placed in the po subdirectory of the package with a name thatis either ‘ll.po’ or ‘R-ll.po’ for translations of the C and R messages respectively to languagewith code ‘ll’.

See Section “Localization of messages” in R Installation and Administration, for details oflanguage codes.

There is an R function, update_pkg_po in package tools, to automate much of the mainte-nance of message translations. See its help for what it does in detail.

If this is called on a package with no existing translations, it creates the directory pkgdir/po,creates a template file of R messages, pkgdir/po/R-pkg.pot, within it, creates the ‘en@quot’translation and installs that. (The ‘en@quot’ pseudo-language interprets quotes in their direc-tional forms in suitable (e.g. UTF-8) locales.)

If the package has C source files in its src directory that are marked for translation, use

touch pkgdir/po/pkg.pot

to create a dummy template file, then call update_pkg_po again (this can also be done beforeit is called for the first time).

When translations to new languages are added in the pkgdir/po directory, running the samecommand will check and then install the translations.

If the package sources are updated, the same command will update the template files, mergethe changes into the translation .po files and then installed the updated translations. Youwill often see that merging marks translations as ‘fuzzy’ and this is reported in the coverage

Chapter 1: Creating R packages 48

statistics. As fuzzy translations are not used, this is an indication that the translation files needhuman attention.

The merged translations are run through tools::checkPofile to check that C-style formatsare used correctly: if not the mismatches are reported and the broken translations are notinstalled.

This function needs the GNU gettext-tools installed and on the path: see its help page.

1.9 CITATION files

An installed file named CITATION will be used by the citation() function. (To be installed, itneeded to be in the inst subdirectory of the package sources.)

The CITATION file is parsed as R code (in the package’s declared encoding, or in ASCII if noneis declared). If no such file is present, citation auto-generates citation information from thepackage DESCRIPTION metadata, and an example of what that would look like as a CITATION filecan be seen in recommended package nlme (see below): recommended packages boot, clusterand mgcv have further examples.

A CITATION file will contain calls to function bibentry.

Here is that for nlme, re-formatted:

citHeader("To cite package ’nlme’ in publications use:")

year <- sub(".*(2[[:digit:]]{3})-.*", "\\1", meta$Date, perl = TRUE)

vers <- paste("R package version", meta$Version)

citEntry(entry = "Manual",

title = "nlme: Linear and Nonlinear Mixed Effects Models",

author = personList(as.person("Jose Pinheiro"),

as.person("Douglas Bates"),

as.person("Saikat DebRoy"),

as.person("Deepayan Sarkar"),

person("R Core Team")),

year = year,

note = vers,

textVersion =

paste0("Jose Pinheiro, Douglas Bates, Saikat DebRoy,",

"Deepayan Sarkar and the R Core Team (",

year,

"). nlme: Linear and Nonlinear Mixed Effects Models. ",

vers, "."))

Note the way that information that may need to be updated is picked up from theDESCRIPTION file – it is tempting to hardcode such information, but it normally then getsoutdated. See ?bibentry for further details of the information which can be provided.

The CITATION file should itself produce no output when source-d.

1.10 Package types

The DESCRIPTION file has an optional field Type which if missing is assumed to be ‘Package’,the sort of extension discussed so far in this chapter. Currently one other type is recognized;there used also to be a ‘Translation’ type.

Chapter 1: Creating R packages 49

1.10.1 Frontend

This is a rather general mechanism, designed for adding new front-ends such as the formergnomeGUI package (see the Archive area on CRAN). If a configure file is found in the top-level directory of the package it is executed, and then if a Makefile is found (often generated byconfigure), make is called. If R CMD INSTALL --clean is used make clean is called. No otheraction is taken.

R CMD build can package up this type of extension, but R CMD check will check the type andskip it.

Many packages of this type need write permission for the R installation directory.

1.11 Services

Several members of the R project have set up services to assist those writing R packages,particularly those intended for public distribution.

win-builder.r-project.org offers the automated preparation of (32/64-bit) Windows binariesfrom well-tested source packages.

R-Forge (R-Forge.r-project.org) and RForge (www.rforge.net) are similar services with sim-ilar names. Both provide source-code management through SVN, daily building and checking,mailing lists and a repository that can be accessed via install.packages (they can be selectedby setRepositories and the GUI menus that use it). Package developers have the opportunityto present their work on the basis of project websites or news announcements. Mailing lists,forums or wikis provide useRs with convenient instruments for discussions and for exchanginginformation between developers and/or interested useRs.

Chapter 2: Writing R documentation files 50

2 Writing R documentation files

2.1 Rd format

R objects are documented in files written in “R documentation” (Rd) format, a simple markuplanguage much of which closely resembles (La)TEX, which can be processed into a variety offormats, including LATEX, HTML and plain text. The translation is carried out by functions inthe tools package called by the script Rdconv in R_HOME/bin and by the installation scripts forpackages.

The R distribution contains more than 1300 such files which can be found in thesrc/library/pkg/man directories of the R source tree, where pkg stands for one of thestandard packages which are included in the R distribution.

As an example, let us look at a simplified version of src/library/base/man/load.Rd whichdocuments the R function load.� �

% File src/library/base/man/load.Rd

\name{load}

\alias{load}

\title{Reload Saved Datasets}

\description{

Reload the datasets written to a file with the function

\code{save}.

}

\usage{

load(file, envir = parent.frame())

}

\arguments{

\item{file}{a connection or a character string giving the

name of the file to load.}

\item{envir}{the environment where the data should be

loaded.}

}

\seealso{

\code{\link{save}}.

}

\examples{

## save all data

save(list = ls(), file= "all.RData")

## restore the saved values to the current environment

load("all.RData")

## restore the saved values to the workspace

load("all.RData", .GlobalEnv)

}

\keyword{file} An Rd file consists of three parts. The header gives basic information about the name of

the file, the topics documented, a title, a short textual description and R usage information forthe objects documented. The body gives further information (for example, on the function’sarguments and return value, as in the above example). Finally, there is an optional footer withkeyword information. The header is mandatory.

Information is given within a series of sections with standard names (and user-defined sectionsare also allowed). Unless otherwise specified1 these should occur only once in an Rd file (in any

1 e.g. \alias, \keyword and \note sections.

Chapter 2: Writing R documentation files 51

order), and the processing software will retain only the first occurrence of a standard section inthe file, with a warning.

See “Guidelines for Rd files” for guidelines for writing documentation in Rd format whichshould be useful for package writers. The R generic function prompt is used to construct a bare-bones Rd file ready for manual editing. Methods are defined for documenting functions (whichfill in the proper function and argument names) and data frames. There are also functionspromptData, promptPackage, promptClass, and promptMethods for other types of Rd file.

The general syntax of Rd files is summarized below. For a detailed technical discussion ofcurrent Rd syntax, see “Parsing Rd files”.

Rd files consists of three types of text input. The most common is LATEX-like, with thebackslash used as a prefix on markup (e.g. \alias), and braces used to indicate arguments (e.g.{load}). The least common type of text is verbatim text, where no markup is processed. Thethird type is R-like, intended for R code, but allowing some embedded macros. Quoted stringswithin R-like text are handled specially: regular character escapes such as \n may be enteredas-is. Only markup starting with \l (e.g. \link) or \v (e.g. \var) will be recognized withinquoted strings. The rarely used vertical tab \v must be entered as \\v.

Each macro defines the input type for its argument. For example, the file initially usesLATEX-like syntax, and this is also used in the \description section, but the \usage sectionuses R-like syntax, and the \alias macro uses verbatim syntax. Comments run from a percentsymbol % to the end of the line in all types of text (as on the first line of the load example).

Because backslashes, braces and percent symbols have special meaning, to enter them intotext sometimes requires escapes using a backslash. In general balanced braces do not need to beescaped, but percent symbols always do. For the complete list of macros and rules for escapes,see “Parsing Rd files”.

2.1.1 Documenting functions

The basic markup commands used for documenting R objects (in particular, functions) are givenin this subsection.

\name{name}

name typically2 is the basename of the Rd file containing the documentation. Itis the “name” of the Rd object represented by the file and has to be unique in apackage. To avoid problems with indexing the package manual, it may not contain‘!’ ‘|’ nor ‘@’, and to avoid possible problems with the HTML help system it shouldnot contain ‘/’ nor a space. (LATEX special characters are allowed, but may not becollated correctly in the index.) There can only be one \name entry in a file, and itmust not contain any markup. Entries in the package manual will be in alphabetic3

order of the \name entries.

\alias{topic}

The \alias sections specify all “topics” the file documents. This information iscollected into index data bases for lookup by the on-line (plain text and HTML)help systems. The topic can contain spaces, but (for historical reasons) leading andtrailing spaces will be stripped. Percent and left brace need to be escaped by abackslash.

There may be several \alias entries. Quite often it is convenient to documentseveral R objects in one file. For example, file Normal.Rd documents the density,

2 There can be exceptions: for example Rd files are not allowed to start with a dot, and have to be uniquelynamed on a case-insensitive file system.

3 in the current locale, and with special treatment for LATEX special characters and with any ‘pkgname-package’topic moved to the top of the list.

Chapter 2: Writing R documentation files 52

distribution function, quantile function and generation of random variates for thenormal distribution, and hence starts with

\name{Normal}

\alias{Normal}

\alias{dnorm}

\alias{pnorm}

\alias{qnorm}

\alias{rnorm}

Also, it is often convenient to have several different ways to refer to an R object,and an \alias does not need to be the name of an object.

Note that the \name is not necessarily a topic documented, and if so desired it needsto have an explicit \alias entry (as in this example).

\title{Title}

Title information for the Rd file. This should be capitalized and not end in a period;try to limit its length to at most 65 characters for widest compatibility.

Markup is supported in the text, but use of characters other than English text andpunctuation (e.g., ‘<’) may limit portability.

There must be one (and only one) \title section in a help file.

\description{...}

A short description of what the function(s) do(es) (one paragraph, a few lines only).(If a description is too long and cannot easily be shortened, the file probably tries todocument too much at once.) This is mandatory except for package-overview files.

\usage{fun(arg1, arg2, ...)}

One or more lines showing the synopsis of the function(s) and variables documentedin the file. These are set in typewriter font. This is an R-like command.

The usage information specified should match the function definition exactly (suchthat automatic checking for consistency between code and documentation is possi-ble).

It is no longer advisable to use \synopsis for the actual synopsis and show modifiedsynopses in the \usage. Support for \synopsis will be removed in \R 3.1.0. Toindicate that a function can be used in several different ways, depending on thenamed arguments specified, use section \details. E.g., abline.Rd contains

\details{

Typical usages are

\preformatted{abline(a, b, untf = FALSE, \dots)

......

}

Use \method{generic}{class} to indicate the name of an S3 method for the genericfunction generic for objects inheriting from class "class". In the printed versions,this will come out as generic (reflecting the understanding that methods should notbe invoked directly but via method dispatch), but codoc() and other QC toolsalways have access to the full name.

For example, print.ts.Rd contains

\usage{

\method{print}{ts}(x, calendar, \dots)

}

which will print as

Chapter 2: Writing R documentation files 53

Usage:

## S3 method for class ’ts’:

print(x, calendar, ...)

Usage for replacement functions should be given in the style of dim(x) <- value

rather than explicitly indicating the name of the replacement function ("dim<-" inthe above). Similarly, one can use \method{generic}{class}(arglist) <- value

to indicate the usage of an S3 replacement method for the generic replacementfunction "generic<-" for objects inheriting from class "class".

Usage for S3 methods for extracting or replacing parts of an object, S3 methods formembers of the Ops group, and S3 methods for user-defined (binary) infix opera-tors (‘%xxx%’) follows the above rules, using the appropriate function names. E.g.,Extract.factor.Rd contains

\usage{

\method{[}{factor}(x, \dots, drop = FALSE)

\method{[[}{factor}(x, \dots)

\method{[}{factor}(x, \dots) <- value

}

which will print as

Usage:

## S3 method for class ’factor’:

x[..., drop = FALSE]

## S3 method for class ’factor’:

x[[...]]

## S3 replacement method for class ’factor’:

x[...] <- value

\S3method is accepted as an alternative to \method.

\arguments{...}

Description of the function’s arguments, using an entry of the form

\item{arg_i}{Description of arg_i.}

for each element of the argument list. (Note that there is no whitespace betweenthe three parts of the entry.) There may be optional text outside the \item entries,for example to give general information about groups of parameters.

\details{...}

A detailed if possible precise description of the functionality provided, extendingthe basic information in the \description slot.

\value{...}

Description of the function’s return value.

If a list with multiple values is returned, you can use entries of the form

\item{comp_i}{Description of comp_i.}

for each component of the list returned. Optional text may precede4 this list (seefor example the help for rle). Note that \value is implicitly a \describe environ-ment, so that environment should not be used for listing components, just individual\item{}{} entries.

4 Text between or after list items is discouraged.

Chapter 2: Writing R documentation files 54

\references{...}

A section with references to the literature. Use \url{} or \href{}{} for web point-ers.

\note{...}

Use this for a special note you want to have pointed out. Multiple \note sectionsare allowed, but might be confusing to the end users.

For example, pie.Rd contains

\note{

Pie charts are a very bad way of displaying information.

The eye is good at judging linear measures and bad at

judging relative areas.

......

}

\author{...}

Information about the author(s) of the Rd file. Use \email{} without extra delim-iters (such as ‘( )’ or ‘< >’) to specify email addresses, or \url{} or \href{}{} forweb pointers.

\seealso{...}

Pointers to related R objects, using \code{\link{...}} to refer to them (\code isthe correct markup for R object names, and \link produces hyperlinks in outputformats which support this. See Section 2.3 [Marking text], page 57, and Section 2.5[Cross-references], page 60).

\examples{...}

Examples of how to use the function. Code in this section is set in typewriter fontwithout reformatting and is run by example() unless marked otherwise (see below).

Examples are not only useful for documentation purposes, but also provide test codeused for diagnostic checking of R code. By default, text inside \examples{} willbe displayed in the output of the help page and run by example() and by R CMD

check. You can use \dontrun{} for text that should only be shown, but not run,and \dontshow{} for extra commands for testing that should not be shown to users,but will be run by example(). (Previously this was called \testonly, and that isstill accepted.)

Text inside \dontrun{} is verbatim, but the other parts of the \examples sectionare R-like text.

For example,

x <- runif(10) # Shown and run.\dontrun{plot(x)} # Only shown.\dontshow{log(x)} # Only run.

Thus, example code not included in \dontrun must be executable! In addition, itshould not use any system-specific features or require special facilities (such as In-ternet access or write permission to specific directories). Text included in \dontrun

is indicated by comments in the processed help files: it need not be valid R codebut the escapes must still be used for %, \ and unpaired braces as in other verbatimtext.

Example code must be capable of being run by example, which uses source. Thismeans that it should not access stdin, e.g. to scan() data from the example file.

Data needed for making the examples executable can be obtained by random numbergeneration (for example, x <- rnorm(100)), or by using standard data sets listedby data() (see ?data for more info).

Chapter 2: Writing R documentation files 55

Finally, there is \donttest, used (at the beginning of a separate line) to mark codethat should be run by examples() but not by R CMD check. This should be neededonly occasionally but can be used for code which might fail in circumstances thatare hard to test for, for example in some locales. (Use e.g. capabilities() to testfor features needed in the examples wherever possible, and you can also use try()

or tryCatch().)

\keyword{key}

There can be zero or more \keyword sections per file. Each \keyword sectionshould specify a single keyword, preferably one of the standard keywords as listedin file KEYWORDS in the R documentation directory (default R_HOME/doc). Use e.g.RShowDoc("KEYWORDS") to inspect the standard keywords from within R. There canbe more than one \keyword entry if the R object being documented falls into morethan one category, or none.

The special keyword ‘internal’ marks a page of internal objects that are not partof the package’s API. If the help page for object foo has keyword ‘internal’, thenhelp(foo) gives this help page, but foo is excluded from several object indices,including the alphabetical list of objects in the HTML help system.

help.search() can search by keyword, including user-defined values: however the‘Search Engine & Keywords’ HTML page accessed via help.start() provides single-click access only to a pre-defined list of keywords.

2.1.2 Documenting data sets

The structure of Rd files which document R data sets is slightly different. Sections such as\arguments and \value are not needed but the format and source of the data should be ex-plained.

As an example, let us look at src/library/datasets/man/rivers.Rd which documents thestandard R data set rivers.� �

\name{rivers}

\docType{data}

\alias{rivers}

\title{Lengths of Major North American Rivers}

\description{

This data set gives the lengths (in miles) of 141 \dQuote{major}

rivers in North America, as compiled by the US Geological

Survey.

}

\usage{rivers}

\format{A vector containing 141 observations.}

\source{World Almanac and Book of Facts, 1975, page 406.}

\references{

McNeil, D. R. (1977) \emph{Interactive Data Analysis}.

New York: Wiley.

}

\keyword{datasets} This uses the following additional markup commands.

\docType{...}

Indicates the “type” of the documentation object. Always ‘data’ for data sets, and‘package’ for pkg-package.Rd overview files. Documentation for S4 methods andclasses uses ‘methods’ (from promptMethods()) and ‘class’ (from promptClass()).

Chapter 2: Writing R documentation files 56

\format{...}

A description of the format of the data set (as a vector, matrix, data frame, timeseries, . . . ). For matrices and data frames this should give a description of eachcolumn, preferably as a list or table. See Section 2.4 [Lists and tables], page 59, formore information.

\source{...}

Details of the original source (a reference or URL). In addition, section \references

could give secondary sources and usages.

Note also that when documenting data set bar,

• The \usage entry is always bar or (for packages which do not use lazy-loading of data)data(bar). (In particular, only document a single data object per Rd file.)

• The \keyword entry should always be ‘datasets’.

If bar is a data frame, documenting it as a data set can be initiated via prompt(bar).Otherwise, the promptData function may be used.

2.1.3 Documenting S4 classes and methods

There are special ways to use the ‘?’ operator, namely ‘class?topic’ and ‘methods?topic’,to access documentation for S4 classes and methods, respectively. This mechanism depends onconventions for the topic names used in \alias entries. The topic names for S4 classes andmethods respectively are of the form

class-class

generic,signature_list-method

where signature list contains the names of the classes in the signature of the method (withoutquotes) separated by ‘,’ (without whitespace), with ‘ANY’ used for arguments without an explicitspecification. E.g., ‘genericFunction-class’ is the topic name for documentation for the S4class "genericFunction", and ‘coerce,ANY,NULL-method’ is the topic name for documentationfor the S4 method for coerce for signature c("ANY", "NULL").

Skeletons of documentation for S4 classes and methods can be generated by using the func-tions promptClass() and promptMethods() from package methods. If it is necessary or desiredto provide an explicit function declaration (in a \usage section) for an S4 method (e.g., if it has“surprising arguments” to be mentioned explicitly), one can use the special markup

\S4method{generic}{signature_list}(argument_list)

(e.g., ‘\S4method{coerce}{ANY,NULL}(from, to)’).

To make full use of the potential of the on-line documentation system, all user-visible S4classes and methods in a package should at least have a suitable \alias entry in one of thepackage’s Rd files. If a package has methods for a function defined originally somewhere else,and does not change the underlying default method for the function, the package is responsiblefor documenting the methods it creates, but not for the function itself or the default method.

An S4 replacement method is documented in the same way as an S3 one: see the descriptionof \method in Section 2.1.1 [Documenting functions], page 51.

See help("Documentation", package = "methods") for more information on using and cre-ating on-line documentation for S4 classes and methods.

2.1.4 Documenting packages

Packages may have an overview help page with an \alias pkgname-package, e.g.‘utils-package’ for the utils package, when package?pkgname will open that help page. If atopic named pkgname does not exist in another Rd file, it is helpful to use this as an additional\alias.

Chapter 2: Writing R documentation files 57

Skeletons of documentation for a package can be generated using the functionpromptPackage(). If the final = TRUE argument is used, then the Rd file will be generatedin final form, containing the information that would be produced up to library(help =

pkgname). Otherwise (the default) comments will be inserted giving suggestions for content.

Apart from the mandatory \name and \title and the pkgname-package alias, the onlyrequirement for the package overview page is that it include a \docType{package} statement.All other content is optional. We suggest that it should be a short overview, to give a readerunfamiliar with the package enough information to get started. More extensive documentation isbetter placed into a package vignette (see Section 1.4 [Writing package vignettes], page 31) andreferenced from this page, or into individual man pages for the functions, datasets, or classes.

2.2 Sectioning

To begin a new paragraph or leave a blank line in an example, just insert an empty line (as in(La)TEX). To break a line, use \cr.

In addition to the predefined sections (such as \description{}, \value{}, etc.), you can“define” arbitrary ones by \section{section_title}{...}. For example

\section{Warning}{

You must not call this function unless ...

}

For consistency with the pre-assigned sections, the section name (the first argument to \section)should be capitalized (but not all upper case). Whitespace between the first and second bracedexpressions is not allowed. Markup (e.g. \code) within the section title may cause problemswith the latex conversion (depending on the version of macro packages such as ‘hyperref’) andso should be avoided.

The \subsection macro takes arguments in the same format as \section, but is used withina section, so it may be used to nest subsections within sections or other subsections. There isno predefined limit on the nesting level, but formatting is not designed for more than 3 levels(i.e. subsections within subsections within sections).

Note that additional named sections are always inserted at a fixed position in the output(before \note, \seealso and the examples), no matter where they appear in the input (but inthe same order amongst themselves as in the input).

2.3 Marking text

The following logical markup commands are available for emphasizing or quoting text.

\emph{text}

\strong{text}

Emphasize text using italic and bold font if possible; \strong is regarded as stronger(more emphatic).

\bold{text}

Set text in bold font if possible.

\sQuote{text}

\dQuote{text}

Portably single or double quote text (without hard-wiring the characters used forquotation marks).

Each of the above commands takes LATEX-like input, so other macros may be used withintext.

Chapter 2: Writing R documentation files 58

The following logical markup commands are available for indicating specific kinds of text.Except as noted, these take verbatim text input, and so other macros may not be used withinthem. Some characters will need to be escaped (see Section 2.8 [Insertions], page 61).

\code{text}

Indicate text that is a literal example of a piece of an R program, e.g., a fragment ofR code or the name of an R object. Text is entered in R-like syntax, and displayedusing typewriter font if possible. Macros \var and \link are interpreted withintext.

\preformatted{text}

Indicate text that is a literal example of a piece of a program. Text is displayedusing typewriter font if possible. Formatting, e.g. line breaks, is preserved. (Notethat this includes a line break after the initial {, so typically text should start onthe same line as the command.)

Due to limitations in LATEX as of this writing, this macro may not be nested withinother markup macros other than \dQuote and \sQuote, as errors or bad formattingmay result.

\kbd{keyboard-characters}

Indicate keyboard input, using slanted typewriter font if possible, so users candistinguish the characters they are supposed to type from computer output. Textis entered verbatim.

\samp{text}

Indicate text that is a literal example of a sequence of characters, entered verbatim.No wrapping or reformatting will occur. Displayed using typewriter font if possible.

\verb{text}

Indicate text that is a literal example of a sequence of characters, with no interpre-tation of e.g. \var, but which will be included within word-wrapped text. Displayedusing typewriter font if possible.

\pkg{package_name}

Indicate the name of an R package. LATEX-like.

\file{file_name}

Indicate the name of a file. Text is LATEX-like, so backslash needs to be escaped.Displayed using a distinct font if possible.

\email{email_address}

Indicate an electronic mail address. LATEX-like, will be rendered as a hyperlink inHTML and PDF conversion. Displayed using typewriter font if possible.

\url{uniform_resource_locator}

Indicate a uniform resource locator (URL) for the World Wide Web. The argumentis handled verbatim, and rendered as a hyperlink in HTML and PDF conversion.Displayed using typewriter font if possible.

\href{uniform_resource_locator}{text}

Indicate a hyperlink to the World Wide Web. The first argument is handled verba-tim, and is used as the URL in the hyperlink, with the second argument of LATEX-liketext displayed to the user.

\var{metasyntactic_variable}

Indicate a metasyntactic variable. In some cases this will be rendered distinctly, e.g.in italic, but not in all5. LATEX-like.

5 Currently it is rendered differently only in HTML conversions, and LATEX conversion outside ‘\usage’ and‘\examples’ environments.

Chapter 2: Writing R documentation files 59

\env{environment_variable}

Indicate an environment variable. Verbatim. Displayed using typewriter font ifpossible

\option{option}

Indicate a command-line option. Verbatim. Displayed using typewriter font ifpossible.

\command{command_name}

Indicate the name of a command. LATEX-like, so \var is interpreted. Displayedusing typewriter font if possible.

\dfn{term}

Indicate the introductory or defining use of a term. LATEX-like.

\cite{reference}

Indicate a reference without a direct cross-reference via \link (see Section 2.5[Cross-references], page 60), such as the name of a book. LATEX-like.

\acronym{acronym}

Indicate an acronym (an abbreviation written in all capital letters), such as GNU.LATEX-like.

2.4 Lists and tables

The \itemize and \enumerate commands take a single argument, within which there may beone or more \item commands. The text following each \item is formatted as one or more para-graphs, suitably indented and with the first paragraph marked with a bullet point (\itemize)or a number (\enumerate).

Note that unlike argument lists, \item in these formats is followed by a space and the text(not enclosed in braces). For example

\enumerate{

\item A database consists of one or more records, each with one or

more named fields.

\item Regular lines start with a non-whitespace character.

\item Records are separated by one or more empty lines.

}

\itemize and \enumerate commands may be nested.

The \describe command is similar to \itemize but allows initial labels to be specified.Each \item takes two arguments, the label and the body of the item, in exactly the same wayas an argument or value \item. \describe commands are mapped to <DL> lists in HTML and\description lists in LATEX.

The \tabular command takes two arguments. The first gives for each of the columns therequired alignment (‘l’ for left-justification, ‘r’ for right-justification or ‘c’ for centring.) Thesecond argument consists of an arbitrary number of lines separated by \cr, and with fieldsseparated by \tab. For example:

\tabular{rlll}{

[,1] \tab Ozone \tab numeric \tab Ozone (ppb)\cr

[,2] \tab Solar.R \tab numeric \tab Solar R (lang)\cr

[,3] \tab Wind \tab numeric \tab Wind (mph)\cr

[,4] \tab Temp \tab numeric \tab Temperature (degrees F)\cr

[,5] \tab Month \tab numeric \tab Month (1--12)\cr

[,6] \tab Day \tab numeric \tab Day of month (1--31)

}

Chapter 2: Writing R documentation files 60

There must be the same number of fields on each line as there are alignments in the firstargument, and they must be non-empty (but can contain only spaces). (There is no whitespacebetween \tabular and the first argument, nor between the two arguments.)

2.5 Cross-references

The markup \link{foo} (usually in the combination \code{\link{foo}}) produces a hyperlinkto the help for foo. Here foo is a topic, that is the argument of \alias markup in another Rdfile (possibly in another package). Hyperlinks are supported in some of the formats to which Rd

files are converted, for example HTML and PDF, but ignored in others, e.g. the text format.

One main usage of \link is in the \seealso section of the help page, see Section 2.1 [Rdformat], page 50.

Note that whereas leading and trailing spaces are stripped when extracting a topic from a\alias, they are not stripped when looking up the topic of a \link.

You can specify a link to a different topic than its name by \link[=dest]{name} which linksto topic dest with name name. This can be used to refer to the documentation for S3/4 classes,for example \code{"\link[=abc-class]{abc}"} would be a way to refer to the documentationof an S4 class "abc" defined in your package, and \code{"\link[=terms.object]{terms}"}

to the S3 "terms" class (in package stats). To make these easy to read in the source file,\code{"\linkS4class{abc}"} expands to the form given above.

There are two other forms of optional argument specified as \link[pkg]{foo} and\link[pkg:bar]{foo} to link to the package pkg , to files foo.html and bar.html respectively.These are rarely needed, perhaps to refer to not-yet-installed packages (but there the HTML

help system will resolve the link at run time) or in the normally undesirable event that morethan one package offers help on a topic6 (in which case the present package has precedence sothis is only needed to refer to other packages). They are currently only used in HTML help(and ignored for hyperlinks in LATEX conversions of help pages), and link to the file ratherthan the topic (since there is no way to know which topics are in which files in an uninstalledpackage). The only reason to use these forms for base and recommended packages is to force areference to a package that might be further down the search path. Because they have beenfrequently misused, the HTML help system looks for topic foo in package pkg if it does notfind file foo.html.

2.6 Mathematics

Mathematical formulae should be set beautifully for printed documentation yet we stillwant something useful for text and HTML online help. To this end, the two commands\eqn{latex}{ascii} and \deqn{latex}{ascii} are used. Whereas \eqn is used for “inline”formulae (corresponding to TEX’s $...$), \deqn gives “displayed equations” (as in LATEX’sdisplaymath environment, or TEX’s $$...$$). Both arguments are treated as verbatim text.

Both commands can also be used as \eqn{latexascii} (only one argument) which thenis used for both latex and ascii. No whitespace is allowed between command and the firstargument, nor between the first and second arguments.

The following example is from Poisson.Rd:

\deqn{p(x) = \frac{\lambda^x e^{-\lambda}}{x!}}{%

p(x) = \lambda^x exp(-\lambda)/x!}

for \eqn{x = 0, 1, 2, \ldots}.

For the LATEX manual, this becomes

6 a common example in CRAN packages is \link[mgcv]{gam}.

Chapter 2: Writing R documentation files 61� �p(x) = λx

e−λ

x!

for x = 0, 1, 2, . . .. For text on-line help we get� �

p(x) = lambda^x exp(-lambda)/x!

for x = 0, 1, 2, .... Greek letters (both cases) will be rendered in HTML if preceded by a backslash, \dots and

\ldots will be rendered as ellipses and \sqrt, \ge and \le as mathematical symbols.

Note that only basic LATEX can be used, there being no provision to specify LATEX style filessuch as the AMS extensions.

2.7 Figures

To include figures in help pages, use the \figure markup. There are three forms.

The two commonly used simple forms are \figure{filename} and\figure{filename}{alternate text}. This will include a copy of the figure in ei-ther HTML or LATEX output. In text output, the alternate text will be displayed instead.(When the second argument is omitted, the filename will be used.) Both the filename andthe alternate text will be parsed verbatim, and should not include special characters that aresignificant in HTML or LATEX.

The expert form is \figure{filename}{options: string}. (The word ‘options:’ must betyped exactly as shown and followed by at least one space.) In this form, the string is copiedinto the HTML img tag as attributes following the src attribute, or into the second argumentof the \Figure macro in LATEX, which by default is used as options to an \includegraphics

call. As it is unlikely that any single string would suffice for both display modes, the expertform would normally be wrapped in conditionals. It is up to the author to make sure that legalHTML/LATEX is used. For example, to include a logo in both HTML (using the simple form) andLATEX (using the expert form), the following could be used:

\if{html}{\figure{logo.jpg}{Our logo}}

\if{latex}{\figure{logo.jpg}{options: width=0.5in}}

The files containing the figures should be stored in the directory man/figures. Files withextensions .jpg, .pdf, .png and .svg from that directory will be copied to the help/figures

directory at install time. (Figures in PDF format will not display in most HTML browsers, butmight be the best choice in reference manuals.) Specify the filename relative to man/figures inthe \figure directive.

2.8 Insertions

Use \R for the R system itself. Use \dots for the dots in function argument lists ‘...’, and\ldots for ellipsis dots in ordinary text.7 These can be followed by {}, and should be unlessfollowed by whitespace.

After an unescaped ‘%’, you can put your own comments regarding the help text. The restof the line (but not the newline at the end) will be completely disregarded. Therefore, you canalso use it to make part of the “help” invisible.

7 There is only a fine distinction between \dots and \ldots. It is technically incorrect to use \ldots in codeblocks and tools::checkRd will warn about this—on the other hand the current converters treat them thesame way in code blocks, and elsewhere apart from the small distinction between the two in LATEX.

Chapter 2: Writing R documentation files 62

You can produce a backslash (‘\’) by escaping it by another backslash. (Note that \cr isused for generating line breaks.)

The “comment” character ‘%’ and unpaired braces8 almost always need to be escaped by ‘\’,and ‘\\’ can be used for backslash and needs to be when there two or more adjacent backslashes).In R-like code quoted strings are handled slightly differently; see “Parsing Rd files” for details– in particular braces should not be escaped in quoted strings.

All of ‘% { } \’ should be escaped in LATEX-like text.

Text which might need to be represented differently in different encodings should be markedby \enc, e.g. \enc{Joreskog}{Joreskog} (with no whitespace between the braces) where thefirst argument will be used where encodings are allowed and the second should be ASCII (andis used for e.g. the text conversion in locales that cannot represent the encoded form). (This isintended to be used for individual words, not whole sentences or paragraphs.)

2.9 Indices

The \alias command (see Section 2.1.1 [Documenting functions], page 51) is used to specifythe “topics” documented, which should include all R objects in a package such as functions andvariables, data sets, and S4 classes and methods (see Section 2.1.3 [Documenting S4 classes andmethods], page 56). The on-line help system searches the index data base consisting of all aliastopics.

In addition, it is possible to provide “concept index entries” using \concept, which can beused for help.search() lookups. E.g., file cor.test.Rd in the standard package stats contains

\concept{Kendall correlation coefficient}

\concept{Pearson correlation coefficient}

\concept{Spearman correlation coefficient}

so that e.g. ??Spearman will succeed in finding the help page for the test for association betweenpaired samples using Spearman’s ρ.

(Note that help.search() only uses “sections” of documentation objects with no additionalmarkup.)

If you want to cross reference such items from other help files via \link, you need to use\alias and not \concept.

2.10 Platform-specific documentation

Sometimes the documentation needs to differ by platform. Currently two OS-specific optionsare available, ‘unix’ and ‘windows’, and lines in the help source file can be enclosed in

#ifdef OS

...

#endif

or

#ifndef OS

...

#endif

for OS-specific inclusion or exclusion. Such blocks should not be nested, and should be entirelywithin a block (that, is between the opening and closing brace of a section or item), or attop-level contain one or more complete sections.

If the differences between platforms are extensive or the R objects documented are onlyrelevant to one platform, platform-specific Rd files can be put in a unix or windows subdirectory.

8 See the examples section in the file Paren.Rd for an example.

Chapter 2: Writing R documentation files 63

2.11 Conditional text

Occasionally the best content for one output format is different from the best content for another.For this situation, the \if{format}{text} or \ifelse{format}{text}{alternate} markup isused. Here format is a comma separated list of formats in which the text should be rendered.The alternate will be rendered if the format does not match. Both text and alternate may beany sequence of text and markup.

Currently the following formats are recognized: example, html, latex and text. These selectoutput for the corresponding targets. (Note that example refers to extracted example coderather than the displayed example in some other format.) Also accepted are TRUE (matchingall formats) and FALSE (matching no formats). These could be the output of the \Sexpr macro(see Section 2.12 [Dynamic pages], page 63).

The \out{literal} macro would usually be used within the text part of\if{format}{text}. It causes the renderer to output the literal text exactly, withno attempt to escape special characters. For example, use the following to output the markupnecessary to display the Greek letter in LATEX or HTML, and the text string alpha in otherformats:

\if{latex}{\out{\alpha}}\ifelse{html}{\out{&alpha;}}{alpha}

2.12 Dynamic pages

Two macros supporting dynamically generated man pages are \Sexpr and \RdOpts. These aremodelled after Sweave, and are intended to contain executable R expressions in the Rd file.

The main argument to \Sexpr must be valid R code that can be executed. It may also takeoptions in square brackets before the main argument. Depending on the options, the code maybe executed at package build time, package install time, or man page rendering time.

The options follow the same format as in Sweave, but different options are supported. Cur-rently the allowed options and their defaults are:

• eval=TRUE Whether the R code should be evaluated.

• echo=FALSE Whether the R code should be echoed. If TRUE, a display will be given in apreformatted block. For example, \Sexpr[echo=TRUE]{ x <- 1 } will be displayed as

> x <- 1

• keep.source=TRUE Whether to keep the author’s formatting when displaying the code, orthrow it away and use a deparsed version.

• results=text How should the results be displayed? The possibilities are:

− results=text Apply as.character() to the result of the code, and insert it as a textelement.

− results=verbatim Print the results of the code just as if it was executed at the console,and include the printed results verbatim. (Invisible results will not print.)

− results=rd The result is assumed to be a character vector containing markup to bepassed to parse_Rd(), with the result inserted in place. This could be used to insertcomputed aliases, for instance. parse_Rd() is called first with fragment = FALSE toallow a single Rd section macro to be inserted. If that fails, it is called again withfragment = TRUE, the older behavior.

− results=hide Insert no output.

• strip.white=TRUE Remove leading and trailing white space from each line of output ifstrip.white=TRUE. With strip.white=all, also remove blank lines.

• stage=install Control when this macro is run. Possible values are

− stage=build The macro is run when building a source tarball.

Chapter 2: Writing R documentation files 64

− stage=install The macro is run when installing from source.

− stage=render The macro is run when displaying the help page.

Conditionals such as #ifdef (see Section 2.10 [Platform-specific sections], page 62) areapplied after the build macros but before the install macros. In some situations (e.g.installing directly from a source directory without a tarball, or building a binary package)the above description is not literally accurate, but authors can rely on the sequence beingbuild, #ifdef, install, render, with all stages executed.

Code is only run once in each stage, so a \Sexpr[results=rd] macro can output an \Sexpr

macro designed for a later stage, but not for the current one or any earlier stage.

• width, height, fig These options are currently allowed but ignored.

The \RdOpts macro is used to set new defaults for options to apply to following uses of\Sexpr.

For more details, see the online document “Parsing Rd files”.

2.13 User-defined macros

The \newcommand and \renewcommand macros allow new macros to be defined within an Rdfile. These are similar but not identical to the same-named LATEX macros.

They each take two arguments which are parsed verbatim. The first is the name of thenew macro including the initial backslash, and the second is the macro definition. As inLATEX, \newcommand requires that the new macro not have been previously defined, whereas\renewcommand allows existing macros (including all built-in ones) to be replaced.

Also as in LATEX, the new macro may be defined to take arguments, and numeric placeholderssuch as #1 are used in the macro definition. However, unlike LATEX, the number of argumentsis determined automatically from the highest placeholder number seen in the macro definition.For example, a macro definition containing #1 and #3 (but no other placeholders) will definea three argument macro (whose second argument will be ignored). As in LATEX, at most 9arguments may be defined. If the # character is followed by a non-digit it will have no specialsignificance. All arguments to user-defined macros will be parsed as verbatim text, and simpletext-substitution will be used to replace the place-holders, after which the replacement text willbe parsed.

For example, the NEWS.Rd file currently uses the definition

\newcommand{\PR}{\Sexpr[results=rd]{tools:::Rd_expr_PR(#1)}}

which defines \PR to be a single argument macro; then code like

\PR{1234}

will expand to

\Sexpr[results=rd]{tools:::Rd_expr_PR(1234)}

when parsed.

2.14 Encoding

Rd files are text files and so it is impossible to deduce the encoding they are written in unlessASCII: files with 8-bit characters could be UTF-8, Latin-1, Latin-9, KOI8-R, EUC-JP, etc. So an\encoding{} section must be used to specify the encoding if it is not ASCII. (The \encoding{}section must be on a line by itself, and in particular one containing no non-ASCII characters.The encoding declared in the DESCRIPTION file will be used if none is declared in the file.) The Rdfiles are converted to UTF-8 before parsing and so the preferred encoding for the files themselvesis now UTF-8.

Chapter 2: Writing R documentation files 65

Wherever possible, avoid non-ASCII chars in Rd files, and even symbols such as ‘<’, ‘>’, ‘$’,‘^’, ‘&’, ‘|’, ‘@’, ‘~’, and ‘*’ outside verbatim environments (since they may disappear in fontsdesigned to render text). (Function showNonASCIIfile in package tools can help in findingnon-ASCII bytes in the files.)

For convenience, encoding names ‘latin1’ and ‘latin2’ are always recognized: these and‘UTF-8’ are likely to work fairly widely. However, this does not mean that all characters inUTF-8 will be recognized, and the coverage of non-Latin characters9 is fairly low. Using LATEXinputenx (see ?Rd2pdf in R) will give greater coverage of UTF-8.

The \enc command (see Section 2.8 [Insertions], page 61) can be used to provide transliter-ations which will be used in conversions that do not support the declared encoding.

The LATEX conversion converts the file to UTF-8 from the declared encoding, and includes a

\inputencoding{utf8}

command, and this needs to be matched by a suitable invocation of the \usepackage{inputenc}command. The R utility R CMD Rd2pdf looks at the converted code and includes the encodingsused: it might for example use

\usepackage[utf8]{inputenc}

(Use of utf8 as an encoding requires LATEX dated 2003/12/01 or later. Also, the use of Cyrilliccharacters in ‘UTF-8’ appears to also need ‘\usepackage[T2A]{fontenc}’, and R CMD Rd2pdf

includes this conditionally on the file t2aenc.def being present and environment variable _R_

CYRILLIC_TEX_ being set.)

Note that this mechanism works best with Latin letters: the coverage of UTF-8 in LATEX isquite low.

2.15 Processing documentation files

There are several commands to process Rd files from the system command line.

Using R CMD Rdconv one can convert R documentation format to other formats, or extractthe executable examples for run-time testing. The currently supported conversions are to plaintext, HTML and LATEX as well as extraction of the examples.

R CMD Rd2pdf generates PDF output from documentation in Rd files, which can be specifiedeither explicitly or by the path to a directory with the sources of a package. In the latter case, areference manual for all documented objects in the package is created, including the informationin the DESCRIPTION files.

R CMD Sweave and R CMD Stangle process ‘Sweave’ documentation files (usually with exten-sion ‘.Snw’ or ‘.Rnw’): R CMD Stangle is use to extract the R code fragments.

The exact usage and a detailed list of available options for all of these commands can be ob-tained by running R CMD command --help, e.g., R CMD Rdconv --help. All available commandscan be listed using R --help (or Rcmd --help under Windows).

All of these work under Windows. You may need to have installed the the tools to buildpackages from source as described in the “R Installation and Administration” manual, althoughtypically all that is needed is a LATEX installation.

9 R 2.9.0 added support for UTF-8 Cyrillic characters in LATEX, but on some OSes this will need Cyrillicsupport added to LATEX, so environment variable _R_CYRILLIC_TEX_ may need to be set to a non-empty valueto enable this.

Chapter 2: Writing R documentation files 66

2.16 Editing Rd files

It can be very helpful to prepare .Rd files using a editor which knows about their syntax andwill highlight commands, indent to show the structure and detect mis-matched braces, and soon.

The system most commonly used for this is some version of Emacs (including XEmacs) withthe ESS package (http://ess.r-project.org/: it is often is installed with Emacs but mayneed to be loaded, or even installed, separately).

Another is the Eclipse IDE with the Stat-ET plugin (http://www.walware.de/goto/statet), and (on Windows only) Tinn-R (http://sourceforge.net/projects/tinn-r/).

People have also used LATEX mode in a editor, as .Rd files are rather similar to LATEX files.

Some R front-ends provide editing support for .Rd files, for example RStudio (http://rstudio.org/).

Chapter 3: Tidying and profiling R code 67

3 Tidying and profiling R code

R code which is worth preserving in a package and perhaps making available for others to useis worth documenting, tidying up and perhaps optimizing. The last two of these activities arethe subject of this chapter.

3.1 Tidying R code

R treats function code loaded from packages and code entered by users differently. By defaultcode entered by users has the source code stored internally, and when the function is listed, theoriginal source is reproduced. Loading code from a package (by default) discards the sourcecode, and the function listing is re-created from the parse tree of the function.

Normally keeping the source code is a good idea, and in particular it avoids comments beingremoved from the source. However, we can make use of the ability to re-create a functionlisting from its parse tree to produce a tidy version of the function, for example with consistentindentation and spaces around operators. If the original source does not follow the standardformat this tidied version can be much easier to read.

We can subvert the keeping of source in two ways.

1. The option keep.source can be set to FALSE before the code is loaded into R.

2. The stored source code can be removed by calling the removeSource() function, for exampleby

myfun <- removeSource(myfun)

In each case if we then list the function we will get the standard layout.

Suppose we have a file of functions myfuns.R that we want to tidy up. Create a file tidy.Rcontaining

source("myfuns.R", keep.source = FALSE)

dump(ls(all = TRUE), file = "new.myfuns.R")

and run R with this as the source file, for example by R --vanilla < tidy.R or by pasting intoan R session. Then the file new.myfuns.R will contain the functions in alphabetical order in thestandard layout. Warning: comments in your functions will be lost.

The standard format provides a good starting point for further tidying. Although the de-parsing cannot do so, we recommend the consistent use of the preferred assignment operator ‘<-’(rather than ‘=’) for assignment. Many package authors use a version of Emacs (on a Unix-alikeor Windows) to edit R code, using the ESS[S] mode of the ESS Emacs package. See Section “Rcoding standards” in R Internals for style options within the ESS[S] mode recommended for thesource code of R itself.

3.2 Profiling R code for speed

It is possible to profile R code on Windows and most1 Unix-alike versions of R.

The command Rprof is used to control profiling, and its help page can be consulted for fulldetails. Profiling works by recording at fixed intervals2 (by default every 20 msecs) which linein which R function is being used, and recording the results in a file (default Rprof.out in theworking directory). Then the function summaryRprof or the command-line utility R CMD Rprof

Rprof.out can be used to summarize the activity.

As an example, consider the following code (from Venables & Ripley, 2002, pp. 225–6).

1 R has to be built to enable this, but the option --enable-R-profiling is the default.2 For Unix-alikes these are intervals of CPU time, and for Windows of elapsed time.

Chapter 3: Tidying and profiling R code 68

library(MASS); library(boot)

storm.fm <- nls(Time ~ b*Viscosity/(Wt - c), stormer,

start = c(b=30.401, c=2.2183))

st <- cbind(stormer, fit=fitted(storm.fm))

storm.bf <- function(rs, i) {

st$Time <- st$fit + rs[i]

tmp <- nls(Time ~ (b * Viscosity)/(Wt - c), st,

start = coef(storm.fm))

tmp$m$getAllPars()

}

rs <- scale(resid(storm.fm), scale = FALSE) # remove the mean

Rprof("boot.out")

storm.boot <- boot(rs, storm.bf, R = 4999) # slow enough to profile

Rprof(NULL)

Having run this we can summarize the results byR CMD Rprof boot.out

Each sample represents 0.02 seconds.

Total run time: 22.52 seconds.

Total seconds: time spent in function and callees.

Self seconds: time spent in function alone.

% total % self

total seconds self seconds name

100.0 25.22 0.2 0.04 "boot"

99.8 25.18 0.6 0.16 "statistic"

96.3 24.30 4.0 1.02 "nls"

33.9 8.56 2.2 0.56 "<Anonymous>"

32.4 8.18 1.4 0.36 "eval"

31.8 8.02 1.4 0.34 ".Call"

28.6 7.22 0.0 0.00 "eval.parent"

28.5 7.18 0.3 0.08 "model.frame"

28.1 7.10 3.5 0.88 "model.frame.default"

17.4 4.38 0.7 0.18 "sapply"

15.0 3.78 3.2 0.80 "nlsModel"

12.5 3.16 1.8 0.46 "lapply"

12.3 3.10 2.7 0.68 "assign"

...

% self % total

self seconds total seconds name

5.7 1.44 7.5 1.88 "inherits"

4.0 1.02 96.3 24.30 "nls"

3.6 0.92 3.6 0.92 "$"

3.5 0.88 28.1 7.10 "model.frame.default"

3.2 0.80 15.0 3.78 "nlsModel"

2.8 0.70 9.8 2.46 "qr.coef"

2.7 0.68 12.3 3.10 "assign"

2.5 0.64 2.5 0.64 ".Fortran"

2.5 0.62 7.1 1.80 "qr.default"

2.2 0.56 33.9 8.56 "<Anonymous>"

2.1 0.54 5.9 1.48 "unlist"

2.1 0.52 7.9 2.00 "FUN"

...

This often produces surprising results and can be used to identify bottlenecks or pieces of Rcode that could benefit from being replaced by compiled code.

Two warnings: profiling does impose a small performance penalty, and the output files canbe very large if long runs are profiled at the default sampling interval.

Profiling short runs can sometimes give misleading results. R from time to time performsgarbage collection to reclaim unused memory, and this takes an appreciable amount of timewhich profiling will charge to whichever function happens to provoke it. It may be useful to

Chapter 3: Tidying and profiling R code 69

compare profiling code immediately after a call to gc() with a profiling run without a precedingcall to gc.

More detailed analysis of the output can be achieved by the tools in the CRAN packagesproftools and profr: in particular these allow call graphs to be studied.

3.3 Profiling R code for memory use

Measuring memory use in R code is useful either when the code takes more memory than isconveniently available or when memory allocation and copying of objects is responsible for slowcode. There are three ways to profile memory use over time in R code. All three requireR to have been compiled with --enable-memory-profiling, which is not the default, but iscurrently used for the OS X and Windows binary distributions. All can be misleading, fordifferent reasons.

In understanding the memory profiles it is useful to know a little more about R’s memoryallocation. Looking at the results of gc() shows a division of memory into Vcells used to storethe contents of vectors and Ncells used to store everything else, including all the administrativeoverhead for vectors such as type and length information. In fact the vector contents are dividedinto two pools. Memory for small vectors (by default 128 bytes or less) is obtained in large chunksand then parcelled out by R; memory for larger vectors is obtained directly from the operatingsystem.

Some memory allocation is obvious in interpreted code, for example,

y <- x + 1

allocates memory for a new vector y. Other memory allocation is less obvious and occurs becauseR is forced to make good on its promise of ‘call-by-value’ argument passing. When an argumentis passed to a function it is not immediately copied. Copying occurs (if necessary) only whenthe argument is modified. This can lead to surprising memory use. For example, in the ‘survey’package we have

print.svycoxph <- function (x, ...)

{

print(x$survey.design, varnames = FALSE, design.summaries = FALSE,

...)

x$call <- x$printcall

NextMethod()

}

It may not be obvious that the assignment to x$call will cause the entire object x to be copied.This copying to preserve the call-by-value illusion is usually done by the internal C functionduplicate.

The main reason that memory-use profiling is difficult is garbage collection. Memory isallocated at well-defined times in an R program, but is freed whenever the garbage collectorhappens to run.

3.3.1 Memory statistics from Rprof

The sampling profiler Rprof described in the previous section can be given the optionmemory.profiling=TRUE. It then writes out the total R memory allocation in small vectors,large vectors, and cons cells or nodes at each sampling interval. It also writes out the numberof calls to the internal function duplicate, which is called to copy R objects. summaryRprof

provides summaries of this information. The main reason that this can be misleading is thatthe memory use is attributed to the function running at the end of the sampling interval. Asecond reason is that garbage collection can make the amount of memory in use decrease, so afunction appears to use little memory. Running under gctorture helps with both problems: itslows down the code to effectively increase the sampling frequency and it makes each garbage

Chapter 3: Tidying and profiling R code 70

collection release a smaller amount of memory. Changing the memory limits with mem.limits()

may also be useful, to see how the code would run under different memory conditions.

3.3.2 Tracking memory allocations

The second method of memory profiling uses a memory-allocation profiler, Rprofmem(), whichwrites out a stack trace to an output file every time a large vector is allocated (with a user-specified threshold for ‘large’) or a new page of memory is allocated for the R heap. Summaryfunctions for this output are still being designed.

Running the example from the previous section with

> Rprofmem("boot.memprof",threshold=1000)

> storm.boot <- boot(rs, storm.bf, R = 4999)

> Rprofmem(NULL)

shows that apart from some initial and final work in boot there are no vector allocations over1000 bytes.

3.3.3 Tracing copies of an object

The third method of memory profiling involves tracing copies made of a specific (presumablylarge) R object. Calling tracemem on an object marks it so that a message is printed to standardoutput when the object is copied via duplicate or coercion to another type, or when a newobject of the same size is created in arithmetic operations. The main reason that this can bemisleading is that copying of subsets or components of an object is not tracked. It may behelpful to use tracemem on these components.

In the example above we can run tracemem on the data frame st

> tracemem(st)

[1] "<0x9abd5e0>"

> storm.boot <- boot(rs, storm.bf, R = 4)

memtrace[0x9abd5e0->0x92a6d08]: statistic boot

memtrace[0x92a6d08->0x92a6d80]: $<-.data.frame $<- statistic boot

memtrace[0x92a6d80->0x92a6df8]: $<-.data.frame $<- statistic boot

memtrace[0x9abd5e0->0x9271318]: statistic boot

memtrace[0x9271318->0x9271390]: $<-.data.frame $<- statistic boot

memtrace[0x9271390->0x9271408]: $<-.data.frame $<- statistic boot

memtrace[0x9abd5e0->0x914f558]: statistic boot

memtrace[0x914f558->0x914f5f8]: $<-.data.frame $<- statistic boot

memtrace[0x914f5f8->0x914f670]: $<-.data.frame $<- statistic boot

memtrace[0x9abd5e0->0x972cbf0]: statistic boot

memtrace[0x972cbf0->0x972cc68]: $<-.data.frame $<- statistic boot

memtrace[0x972cc68->0x972cd08]: $<-.data.frame $<- statistic boot

memtrace[0x9abd5e0->0x98ead98]: statistic boot

memtrace[0x98ead98->0x98eae10]: $<-.data.frame $<- statistic boot

memtrace[0x98eae10->0x98eae88]: $<-.data.frame $<- statistic boot

The object is duplicated fifteen times, three times for each of the R+1 calls to storm.bf. Thisis surprising, since none of the duplications happen inside nls. Stepping through storm.bf inthe debugger shows that all three happen in the line

st$Time <- st$fit + rs[i]

Data frames are slower than matrices and this is an example of why. Usingtracemem(st$Viscosity) does not reveal any additional copying.

3.4 Profiling compiled code

Profiling compiled code is highly system-specific, but this section contains some hints gleanedfrom various R users. Some methods need to be different for a compiled executable and fordynamic/shared libraries/objects as used by R packages. We know of no good way to profileDLLs on Windows.

Chapter 3: Tidying and profiling R code 71

3.4.1 Linux

Options include using sprof for a shared object, and oprofile (see http: / /oprofile .

sourceforge.net/) and perf (see https://perf.wiki.kernel.org/index.php/Tutorial)for any executable or shared object.

3.4.1.1 sprof

You can select shared objects to be profiled with sprof by setting the environment variableLD_PROFILE. For example

% setenv LD_PROFILE /path/to/R_HOME/library/stats/libs/stats.so

R

... run the boot example

% sprof /path/to/R_HOME/library/stats/libs/stats.so \

/var/tmp/path/to/R_HOME/library/stats/libs/stats.so.profile

Flat profile:

Each sample counts as 0.01 seconds.

% cumulative self self total

time seconds seconds calls us/call us/call name

76.19 0.32 0.32 0 0.00 numeric_deriv

16.67 0.39 0.07 0 0.00 nls_iter

7.14 0.42 0.03 0 0.00 getListElement

rm /path/to/R_HOME/library/stats/libs/stats.so.profile

... to clean up ...

It is possible that root access is needed to create the directories used for the profile data.

3.4.1.2 oprofile

oprofile works by running a daemon which collects information. The daemon must be startedas root, e.g.

% su

% opcontrol --no-vmlinux

% (optional, some platforms) opcontrol --callgraph=5

% opcontrol --start

% exit

Then as a user

% R

... run the boot example

% opcontrol --dump

% opreport -l /path/to/R_HOME/library/stats/libs/stats.so

...

samples % symbol name

1623 75.5939 anonymous symbol from section .plt

349 16.2552 numeric_deriv

113 5.2632 nls_iter

62 2.8878 getListElement

% opreport -l /path/to/R_HOME/bin/exec/R

...

samples % symbol name

76052 11.9912 Rf_eval

Chapter 3: Tidying and profiling R code 72

54670 8.6198 Rf_findVarInFrame3

37814 5.9622 Rf_allocVector

31489 4.9649 Rf_duplicate

28221 4.4496 Rf_protect

26485 4.1759 Rf_cons

23650 3.7289 Rf_matchArgs

21088 3.3250 Rf_findFun

19995 3.1526 findVarLocInFrame

14871 2.3447 Rf_evalList

13794 2.1749 R_Newhashpjw

13522 2.1320 R_gc_internal

...

Shutting down the profiler and clearing the records needs to be done as root. You can useopannotate to annotate the source code with the times spent in each section, if the appropriatesource code was compiled with debugging support, and opreport -c to generate a callgraph (ifcollection was enabled and the platform supports this).

3.4.2 Solaris

On 64-bit (only) Solaris, the standard profiling tool gprof collects information from sharedobjects compiled with -pg.

3.4.3 OS X

Developers have recommended sample (or Sampler.app, which is a GUI version), Shark (inversion of Xcode up to those for Snow Leopard), and Instruments (part of Xcode, see https://developer . apple . com / library / mac / # documentation / DeveloperTools / Conceptual /

InstrumentsUserGuide/Introduction/Introduction.html).

Chapter 4: Debugging 73

4 Debugging

This chapter covers the debugging of R extensions, starting with the ways to get useful errorinformation and moving on to how to deal with errors that crash R. For those who prefer otherstyles there are contributed packages such as debug on CRAN (described in an article in R-News3/3). (There are notes from 2002 provided by Roger Peng at http://www.biostat.jhsph.

edu/~rpeng/docs/R-debug-tools.pdf which provide complementary examples to those givenhere.)

4.1 Browsing

Most of the R-level debugging facilities are based around the built-in browser. This can beused directly by inserting a call to browser() into the code of a function (for example, usingfix(my_function) ). When code execution reaches that point in the function, control returnsto the R console with a special prompt. For example

> fix(summary.data.frame) ## insert browser() call after for() loop

> summary(women)

Called from: summary.data.frame(women)

Browse[1]> ls()

[1] "digits" "i" "lbs" "lw" "maxsum" "nm" "nr" "nv"

[9] "object" "sms" "z"

Browse[1]> maxsum

[1] 7

Browse[1]>

height weight

Min. :58.0 Min. :115.0

1st Qu.:61.5 1st Qu.:124.5

Median :65.0 Median :135.0

Mean :65.0 Mean :136.7

3rd Qu.:68.5 3rd Qu.:148.0

Max. :72.0 Max. :164.0

> rm(summary.data.frame)

At the browser prompt one can enter any R expression, so for example ls() lists the objects inthe current frame, and entering the name of an object will1 print it. The following commandsare also accepted

• n

Enter ‘step-through’ mode. In this mode, hitting return executes the next line of code(more precisely one line and any continuation lines). Typing c will continue to the end ofthe current context, e.g. to the end of the current loop or function.

• c

In normal mode, this quits the browser and continues execution, and just return works inthe same way. cont is a synonym.

• where

This prints the call stack. For example

> summary(women)

Called from: summary.data.frame(women)

Browse[1]> where

where 1: summary.data.frame(women)

1 With the exceptions of the commands listed below: an object of such a name can be printed via an explicitcall to print.

Chapter 4: Debugging 74

where 2: summary(women)

Browse[1]>

• Q

Quit both the browser and the current expression, and return to the top-level prompt.

Errors in code executed at the browser prompt will normally return control to the browserprompt. Objects can be altered by assignment, and will keep their changed values when thebrowser is exited. If really necessary, objects can be assigned to the workspace from the browserprompt (by using <<- if the name is not already in scope).

4.2 Debugging R code

Suppose your R program gives an error message. The first thing to find out is what R was doingat the time of the error, and the most useful tool is traceback(). We suggest that this is runwhenever the cause of the error is not immediately obvious. Daily, errors are reported to the Rmailing lists as being in some package when traceback() would show that the error was beingreported by some other package or base R. Here is an example from the regression suite.

> success <- c(13,12,11,14,14,11,13,11,12)

> failure <- c(0,0,0,0,0,0,0,2,2)

> resp <- cbind(success, failure)

> predictor <- c(0, 5^(0:7))

> glm(resp ~ 0+predictor, family = binomial(link="log"))

Error: no valid set of coefficients has been found: please supply starting values

> traceback()

3: stop("no valid set of coefficients has been found: please supply

starting values", call. = FALSE)

2: glm.fit(x = X, y = Y, weights = weights, start = start, etastart = etastart,

mustart = mustart, offset = offset, family = family, control = control,

intercept = attr(mt, "intercept") > 0)

1: glm(resp ~ 0 + predictor, family = binomial(link ="log"))

The calls to the active frames are given in reverse order (starting with the innermost). So wesee the error message comes from an explicit check in glm.fit. (traceback() shows you all thelines of the function calls, which can be limited by setting option "deparse.max.lines".)

Sometimes the traceback will indicate that the error was detected inside compiled code, forexample (from ?nls)

Error in nls(y ~ a + b * x, start = list(a = 0.12345, b = 0.54321), trace = TRUE) :

step factor 0.000488281 reduced below ’minFactor’ of 0.000976563

> traceback()

2: .Call(R_nls_iter, m, ctrl, trace)

1: nls(y ~ a + b * x, start = list(a = 0.12345, b = 0.54321), trace = TRUE)

This will be the case if the innermost call is to .C, .Fortran, .Call, .External or .Internal,but as it is also possible for such code to evaluate R expressions, this need not be the innermostcall, as in

> traceback()

9: gm(a, b, x)

8: .Call(R_numeric_deriv, expr, theta, rho, dir)

7: numericDeriv(form[[3]], names(ind), env)

6: getRHS()

5: assign("rhs", getRHS(), envir = thisEnv)

4: assign("resid", .swts * (lhs - assign("rhs", getRHS(), envir = thisEnv)),

envir = thisEnv)

3: function (newPars)

{

setPars(newPars)

assign("resid", .swts * (lhs - assign("rhs", getRHS(), envir = thisEnv)),

envir = thisEnv)

Chapter 4: Debugging 75

assign("dev", sum(resid^2), envir = thisEnv)

assign("QR", qr(.swts * attr(rhs, "gradient")), envir = thisEnv)

return(QR$rank < min(dim(QR$qr)))

}(c(-0.00760232418963883, 1.00119632515036))

2: .Call(R_nls_iter, m, ctrl, trace)

1: nls(yeps ~ gm(a, b, x), start = list(a = 0.12345, b = 0.54321))

Occasionally traceback() does not help, and this can be the case if S4 method dispatch isinvolved. Consider the following example

> xyd <- new("xyloc", x=runif(20), y=runif(20))

Error in as.environment(pkg) : no item called "package:S4nswv"

on the search list

Error in initialize(value, ...) : S language method selection got

an error when called from internal dispatch for function ’initialize’

> traceback()

2: initialize(value, ...)

1: new("xyloc", x = runif(20), y = runif(20))

which does not help much, as there is no call to as.environment in initialize (and the note“called from internal dispatch” tells us so). In this case we searched the R sources for the quotedcall, which occurred in only one place, methods:::.asEnvironmentPackage. So now we knewwhere the error was occurring. (This was an unusually opaque example.)

The error message

evaluation nested too deeply: infinite recursion / options(expressions=)?

can be hard to handle with the default value (5000). Unless you know that there actually isdeep recursion going on, it can help to set something like

options(expressions=500)

and re-run the example showing the error.

Sometimes there is warning that clearly is the precursor to some later error, but it is notobvious where it is coming from. Setting options(warn = 2) (which turns warnings into errors)can help here.

Once we have located the error, we have some choices. One way to proceed is to find outmore about what was happening at the time of the crash by looking a post-mortem dump. Todo so, set options(error=dump.frames) and run the code again. Then invoke debugger() andexplore the dump. Continuing our example:

> options(error = dump.frames)

> glm(resp ~ 0 + predictor, family = binomial(link ="log"))

Error: no valid set of coefficients has been found: please supply starting values

which is the same as before, but an object called last.dump has appeared in the workspace.(Such objects can be large, so remove it when it is no longer needed.) We can examine this ata later time by calling the function debugger.

> debugger()

Message: Error: no valid set of coefficients has been found: please supply starting values

Available environments had calls:

1: glm(resp ~ 0 + predictor, family = binomial(link = "log"))

2: glm.fit(x = X, y = Y, weights = weights, start = start, etastart = etastart, mus

3: stop("no valid set of coefficients has been found: please supply starting values

Enter an environment number, or 0 to exit Selection:

which gives the same sequence of calls as traceback, but in outer-first order and with only thefirst line of the call, truncated to the current width. However, we can now examine in moredetail what was happening at the time of the error. Selecting an environment opens the browserin that frame. So we select the function call which spawned the error message, and explore someof the variables (and execute two function calls).

Chapter 4: Debugging 76

Enter an environment number, or 0 to exit Selection: 2

Browsing in the environment with call:

glm.fit(x = X, y = Y, weights = weights, start = start, etas

Called from: debugger.look(ind)

Browse[1]> ls()

[1] "aic" "boundary" "coefold" "control" "conv"

[6] "dev" "dev.resids" "devold" "EMPTY" "eta"

[11] "etastart" "family" "fit" "good" "intercept"

[16] "iter" "linkinv" "mu" "mu.eta" "mu.eta.val"

[21] "mustart" "n" "ngoodobs" "nobs" "nvars"

[26] "offset" "start" "valideta" "validmu" "variance"

[31] "varmu" "w" "weights" "x" "xnames"

[36] "y" "ynames" "z"

Browse[1]> eta

1 2 3 4 5

0.000000e+00 -2.235357e-06 -1.117679e-05 -5.588393e-05 -2.794197e-04

6 7 8 9

-1.397098e-03 -6.985492e-03 -3.492746e-02 -1.746373e-01

Browse[1]> valideta(eta)

[1] TRUE

Browse[1]> mu

1 2 3 4 5 6 7 8

1.0000000 0.9999978 0.9999888 0.9999441 0.9997206 0.9986039 0.9930389 0.9656755

9

0.8397616

Browse[1]> validmu(mu)

[1] FALSE

Browse[1]> c

Available environments had calls:

1: glm(resp ~ 0 + predictor, family = binomial(link = "log"))

2: glm.fit(x = X, y = Y, weights = weights, start = start, etastart = etastart

3: stop("no valid set of coefficients has been found: please supply starting v

Enter an environment number, or 0 to exit Selection: 0

> rm(last.dump)

Because last.dump can be looked at later or even in another R session, post-mortem debug-ging is possible even for batch usage of R. We do need to arrange for the dump to be saved: thiscan be done either using the command-line flag --save to save the workspace at the end of therun, or via a setting such as

> options(error = quote({dump.frames(to.file=TRUE); q()}))

See the help on dump.frames for further options and a worked example.

An alternative error action is to use the function recover():

> options(error = recover)

> glm(resp ~ 0 + predictor, family = binomial(link = "log"))

Error: no valid set of coefficients has been found: please supply starting values

Enter a frame number, or 0 to exit

1: glm(resp ~ 0 + predictor, family = binomial(link = "log"))

2: glm.fit(x = X, y = Y, weights = weights, start = start, etastart = etastart

Selection:

which is very similar to dump.frames. However, we can examine the state of the programdirectly, without dumping and re-loading the dump. As its help page says, recover can beroutinely used as the error action in place of dump.calls and dump.frames, since it behaveslike dump.frames in non-interactive use.

Post-mortem debugging is good for finding out exactly what went wrong, but not necessarilywhy. An alternative approach is to take a closer look at what was happening just before the

Chapter 4: Debugging 77

error, and a good way to do that is to use debug. This inserts a call to the browser at thebeginning of the function, starting in step-through mode. So in our example we could use

> debug(glm.fit)

> glm(resp ~ 0 + predictor, family = binomial(link ="log"))

debugging in: glm.fit(x = X, y = Y, weights = weights, start = start, etastart = etastart,

mustart = mustart, offset = offset, family = family, control = control,

intercept = attr(mt, "intercept") > 0)

debug: {

## lists the whole function

Browse[1]>

debug: x <- as.matrix(x)

...

Browse[1]> start

[1] -2.235357e-06

debug: eta <- drop(x %*% start)

Browse[1]> eta

1 2 3 4 5

0.000000e+00 -2.235357e-06 -1.117679e-05 -5.588393e-05 -2.794197e-04

6 7 8 9

-1.397098e-03 -6.985492e-03 -3.492746e-02 -1.746373e-01

Browse[1]>

debug: mu <- linkinv(eta <- eta + offset)

Browse[1]> mu

1 2 3 4 5 6 7 8

1.0000000 0.9999978 0.9999888 0.9999441 0.9997206 0.9986039 0.9930389 0.9656755

9

0.8397616

(The prompt Browse[1]> indicates that this is the first level of browsing: it is possible to stepinto another function that is itself being debugged or contains a call to browser().)

debug can be used for hidden functions and S3 methods by e.g.debug(stats:::predict.Arima). (It cannot be used for S4 methods, but an alter-native is given on the help page for debug.) Sometimes you want to debug a function definedinside another function, e.g. the function arimafn defined inside arima. To do so, set debug onthe outer function (here arima) and step through it until the inner function has been defined.Then call debug on the inner function (and use c to get out of step-through mode in the outerfunction).

To remove debugging of a function, call undebug with the argument previously given todebug; debugging otherwise lasts for the rest of the R session (or until the function is edited orotherwise replaced).

trace can be used to temporarily insert debugging code into a function, for example to inserta call to browser() just before the point of the error. To return to our running example

## first get a numbered listing of the expressions of the function

> page(as.list(body(glm.fit)), method="print")

> trace(glm.fit, browser, at=22)

Tracing function "glm.fit" in package "stats"

[1] "glm.fit"

> glm(resp ~ 0 + predictor, family = binomial(link ="log"))

Tracing glm.fit(x = X, y = Y, weights = weights, start = start,

etastart = etastart, .... step 22

Called from: eval(expr, envir, enclos)

Browse[1]> n

## and single-step from here.

> untrace(glm.fit)

For your own functions, it may be as easy to use fix to insert temporary code, but trace can helpwith functions in a namespace (as can fixInNamespace). Alternatively, use trace(,edit=TRUE)to insert code visually.

Chapter 4: Debugging 78

4.3 Using gctorture and memory access

Errors in memory allocation and reading/writing outside arrays are very common causes ofcrashes (e.g., segfaults) on some machines. Often the crash appears long after the invalid memoryaccess: in particular damage to the structures which R itself has allocated may only becomeapparent at the next garbage collection (or even at later garbage collections after objects havebeen deleted).

Note that memory access errors may be seen with LAPACK, BLAS and Java-using packages:some at least of these seem to be intentional.

There are other memory-access-checking tools available: GCC supports mudflap and‘lightweight bounds checking’ (‘LBC’) is promised soon as a plug-in.

4.3.1 Using gctorture

We can help to detect memory problems earlier by running garbage collection as often as possible.This is achieved by gctorture(TRUE), which as described on its help page

Provokes garbage collection on (nearly) every memory allocation. Intended to ferretout memory protection bugs. Also makes R run very slowly, unfortunately.

The reference to ‘memory protection’ is to missing C-level calls to PROTECT/UNPROTECT (seeSection 5.9.1 [Garbage Collection], page 98) which if missing allow R objects to be garbage-collected when they are still in use. But it can also help with other memory-related errors.

Normally running under gctorture(TRUE) will just produce a crash earlier in the R program,hopefully close to the actual cause. See the next section for how to decipher such crashes.

It is possible to run all the examples, tests and vignettes covered by R CMD check undergctorture(TRUE) by using the option --use-gct.

The function gctorture2 provides more refined control over the GC torture process. Its ar-guments step, wait and inhibit_release are documented on its help page. Environment vari-ables can also be used to turn on GC torture: R_GCTORTURE corresponds to the step argumentto gctorture, R_GCTORTURE_WAIT to wait, and R_GCTORTURE_INHIBIT_RELEASE to inhibit_

release.

If R is configured with --enable-strict-barrier then a variety of tests for the integrity ofthe write barrier are enabled. In addition tests to help detect protect issues are enabled as well:

• All GCs are full GCs.

• New nodes in small node pages are marked as NEWSXP on creation.

• After a GC all free nodes that are not of type NEWSXP are marked as type FREESXP andtheir previous type is recorded.

• Most calls to accessor functions check their SEXP inputs and SEXP outputs and signal anerror if a FREESXP is found. The address of the node and the old type are included in theerror message.

Used with a debugger and with gctorture or gctorture2 this mechanism can be helpful inisolating memory protect problems.

4.3.2 Using valgrind

If you have access to Linux on an ‘ix86’, ‘x86_64’, ‘ppc32’, ‘ppc64’ or ‘s390x’ platform, or(some versions of) OS X you can use valgrind (http://www.valgrind.org/, pronounced torhyme with ‘tinned’) to check for possible problems. To run some examples under valgrind usesomething like

R -d valgrind --vanilla < mypkg-Ex.R

R -d "valgrind --tool=memcheck --leak-check=full" --vanilla < mypkg-Ex.R

Chapter 4: Debugging 79

where mypkg-Ex.R is a set of examples, e.g. the file created in mypkg.Rcheck by R CMD check.Occasionally this reports memory reads of ‘uninitialised values’ that are the result of compileroptimization, so can be worth checking under an unoptimized compile: for maximal informationuse a build with debugging symbols. We know there will be some small memory leaks fromreadline and R itself — these are memory areas that are in use right up to the end of the Rsession. Expect this to run around 20x slower than without valgrind, and in some cases evenslower than that. Several versions of valgrind were not happy with some optimized BLASesthat use CPU-specific instructions so you may need to build a version of R specifically to usewith valgrind.

On Mac OS 10.6 and later the valgrind session is liable to hang when R terminates withvalgrind prior to 3.8.1.

On platforms supported by valgrind you can build a version of R with extra instrumentationto help valgrind detect errors in the use of memory allocated from the R heap. The configureoption is --with-valgrind-instrumentation=level, where level is 0, 1, or 2. Level 0 is thedefault and does not add any anything. Level 1 will detect use of uninitialised memory andhas little impact on speed. Level 2 will detect many other memory-use bugs but makes R muchslower when running under valgrind. Using this in conjunction with gctorture can be evenmore effective (and even slower).

An example of valgrind output is

==12539== Invalid read of size 4

==12539== at 0x1CDF6CBE: csc_compTr (Mutils.c:273)

==12539== by 0x1CE07E1E: tsc_transpose (dtCMatrix.c:25)

==12539== by 0x80A67A7: do_dotcall (dotcode.c:858)

==12539== by 0x80CACE2: Rf_eval (eval.c:400)

==12539== by 0x80CB5AF: R_execClosure (eval.c:658)

==12539== by 0x80CB98E: R_execMethod (eval.c:760)

==12539== by 0x1B93DEFA: R_standardGeneric (methods_list_dispatch.c:624)

==12539== by 0x810262E: do_standardGeneric (objects.c:1012)

==12539== by 0x80CAD23: Rf_eval (eval.c:403)

==12539== by 0x80CB2F0: Rf_applyClosure (eval.c:573)

==12539== by 0x80CADCC: Rf_eval (eval.c:414)

==12539== by 0x80CAA03: Rf_eval (eval.c:362)

==12539== Address 0x1C0D2EA8 is 280 bytes inside a block of size 1996 alloc’d

==12539== at 0x1B9008D1: malloc (vg_replace_malloc.c:149)

==12539== by 0x80F1B34: GetNewPage (memory.c:610)

==12539== by 0x80F7515: Rf_allocVector (memory.c:1915)

...

This example is from an instrumented version of R, while tracking down a bug in the Matrixpackage in January, 2006. The first line indicates that R has tried to read 4 bytes from amemory address that it does not have access to. This is followed by a C stack trace showingwhere the error occurred. Next is a description of the memory that was accessed. It is insidea block allocated by malloc, called from GetNewPage, that is, in the internal R heap. Sincethis memory all belongs to R, valgrind would not (and did not) detect the problem in anuninstrumented build of R. In this example the stack trace was enough to isolate and fix thebug, which was in tsc_transpose, and in this example running under gctorture() did notprovide any additional information. When the stack trace is not sufficiently informative theoption --db-attach=yes to valgrind may be helpful. This starts a post-mortem debugger (bydefault gdb) so that variables in the C code can be inspected (see Section 4.4.2 [Inspecting Robjects], page 82).

valgrind is good at spotting the use of uninitialized values: use option --track-

origins=yes to show where these originated from. What it cannot detect is the misuse ofarrays allocated on the stack: this includes C automatic variables and most Fortran arrays.

Chapter 4: Debugging 80

It is possible to run all the examples, tests and vignettes covered by R CMD check undervalgrind by using the option --use-valgrind. If you do this you will need to select thevalgrind options some other way, for example by having a ~/.valgrindrc file containing

--leak-check=full

--track-origins=yes

or setting the environment variable VALGRIND_OPTS.

On OS X you may need to ensure that debugging symbols are made available (so valgrind re-ports line numbers in files). This can usually be done with the valgrind option --dsymutil=yes

to ask for the symbols to be dumped when the .so file is loaded. This will not work where pack-ages are installed into a system area (such as the R.framework) and can be slow. Installingpackages with R CMD INSTALL --dsym installs the dumped symbols. (This can also be done bysetting environment variable PKG_MAKE_DSYM to a non-empty value before the INSTALL.)

4.3.3 Using AddressSanitizer

AddressSanitizer (‘ASan’) is a tool with similar aims to the memory checker in valgrind.It is available with suitable builds of gcc 4.8.x and clang 3.1 and later2, on common Linuxplatforms and 64-bit Mac OS X. See https://code.google.com/p/address-sanitizer/.

It requires code to have been compiled and linked with -fsanitize=address (gcc and recentversions of clang) or -faddress-sanitizer (clang 3.1), and using -fno-omit-frame-pointerwill give more legible reports. It has a runtime penalty of 2–3x, extended compilation timesand uses substantially more memory, often 1–2GB, at run time. In contrast to valgrind, it candetect misuse of stack variables but not the use of uninitialized memory (although that maycome with -fsanitize=memory in future versions of clang).

Current versions do not return symbolic addresses for the location of the error without somehelp. One possibility is to use an external symbolizer such as asan_symbolize.py from http://

llvm.org: it makes use of llvm-symbolizer if available. For example, the check output can bepiped through asan_symbolize.py and then (for compiled C++ code) c++filt.

The simplest way to make use of this is to build a version of R with something like

CC="gcc-4.8.0 -std=gnu99 -fsanitize=address"

CFLAGS="-fno-omit-frame-pointer -O2 -Wall -pedantic -mtune=native"

which will ensure that the libasan run-time library is compiled into the R executable. However,building R with e.g.

MAIN_LD="gcc-4.8.0 -std=gnu99 -fsanitize=address"

allows this check to be enabled on a per-package basis by using a ~/.R/Makevars file like

CC = gcc-4.8.0 -std=gnu99 -fsanitize=address -fno-omit-frame-pointer

CXX = g++-4.8.0 -fsanitize=address -fno-omit-frame-pointer

F77 = gfortran-4.8.0 -fsanitize=address

FC = gfortran-4.8.0 -fsanitize=address

4.3.4 Fortran array bounds checking

Most of the Fortran compilers used with R allow code to be compiled with checking of arraybounds: for example gfortran has option -fbounds-check and Solaris Studio has -C. Thisgives an error when the upper or lower bound is exceeded, e.g.

At line 97 of file .../src/appl/dqrdc2.f

Fortran runtime error: Index ’1’ of dimension 1 of array ’x’ above upper bound of 0

One does need to be aware that lazy programmers often specify Fortran dimensions as 1

rather than * or a real bound: stats/src/portsrc.f was a prolific example.

2 not including the version distributed by Apple in Xcode 4.6.

Chapter 4: Debugging 81

It is easy to arrange to use this check on just the code in your package: add to ~/.R/Makevarssomething like (for gfortran)

FCFLAGS = -g -O2 -mtune=native -fbounds-check

FFLAGS = -g -O2 -mtune=native -fbounds-check

when you run R CMD check.

4.4 Debugging compiled code

Sooner or later programmers will be faced with the need to debug compiled code loaded intoR. This section is geared to platforms using gdb with code compiled by gcc, but similar thingsare possible with front-ends to gdb such as ddd and insight, and other debuggers such as lldb(used on OS X) and Sun’s dbx.

Consider first ‘crashes’, that is when R terminated unexpectedly with an illegal memoryaccess (a ‘segfault’ or ‘bus error’), illegal instruction or similar. Unix-alike versions of R use asignal handler which aims to give some basic information. For example

*** caught segfault ***

address 0x20000028, cause ’memory not mapped’

Traceback:

1: .identC(class1[[1]], class2)

2: possibleExtends(class(sloti), classi, ClassDef2 = getClassDef(classi,

where = where))

3: validObject(t(cu))

4: stopifnot(validObject(cu <- as(tu, "dtCMatrix")), validObject(t(cu)),

validObject(t(tu)))

Possible actions:

1: abort (with core dump)

2: normal R exit

3: exit R without saving workspace

4: exit R saving workspace

Selection: 3

Since the R process may be damaged, the only really safe option is the first. (Note that a coredump is only produced where enabled: a common default in a shell is to limit its size to 0,thereby disabling it.)

A fairly common cause of such crashes is a package which uses .C or .Fortran and writesbeyond (at either end) one of the arguments it is passed. As from R 3.0.0 there is a good wayto detect this: using options(CBoundsCheck = TRUE) (which can be selected via the environ-ment variable R_C_BOUNDS_CHECK=yes) changes the way .C and .Fortran work to check if thecompiled code writes in the 64 bytes at either end of an argument.

Another cause of a ‘crash’ is to overrun the C stack. R tries to track that in its own code,but it may happen in third-party compiled code. For modern POSIX-compliant OSes R cansafely catch that and return to the top-level prompt, so one gets something like

> .C("aaa")

Error: segfault from C stack overflow

>

However, C stack overflows are fatal under Windows and normally defeat attempts at debuggingon that platform. Further, the size of the stack is set when R is compiled, whereas on POSIXOSes it can be set in the shell from which R is launched.

If you have a crash which gives a core dump you can use something like

Chapter 4: Debugging 82

gdb /path/to/R/bin/exec/R core.12345

to examine the core dump. If core dumps are disabled or to catch errors that do not generate adump one can run R directly under a debugger by for example

$ R -d gdb --vanilla

...

gdb> run

at which point R will run normally, and hopefully the debugger will catch the error and returnto its prompt. This can also be used to catch infinite loops or interrupt very long-running code.For a simple example

> for(i in 1:1e7) x <- rnorm(100)

[hit Ctrl-C]

Program received signal SIGINT, Interrupt.

0x00397682 in _int_free () from /lib/tls/libc.so.6

(gdb) where

#0 0x00397682 in _int_free () from /lib/tls/libc.so.6

#1 0x00397eba in free () from /lib/tls/libc.so.6

#2 0xb7cf2551 in R_gc_internal (size_needed=313)

at /users/ripley/R/svn/R-devel/src/main/memory.c:743

#3 0xb7cf3617 in Rf_allocVector (type=13, length=626)

at /users/ripley/R/svn/R-devel/src/main/memory.c:1906

#4 0xb7c3f6d3 in PutRNGstate ()

at /users/ripley/R/svn/R-devel/src/main/RNG.c:351

#5 0xb7d6c0a5 in do_random2 (call=0x94bf7d4, op=0x92580e8, args=0x9698f98,

rho=0x9698f28) at /users/ripley/R/svn/R-devel/src/main/random.c:183

...

Some “tricks” worth knowing follow:

4.4.1 Finding entry points in dynamically loaded code

Under most compilation environments, compiled code dynamically loaded into R cannot havebreakpoints set within it until it is loaded. To use a symbolic debugger on such dynamicallyloaded code under Unix-alikes use

• Call the debugger on the R executable, for example by R -d gdb.

• Start R.

• At the R prompt, use dyn.load or library to load your shared object.

• Send an interrupt signal. This will put you back to the debugger prompt.

• Set the breakpoints in your code.

• Continue execution of R by typing signal 0RET.

Under Windows signals may not be able to be used, and if so the procedure is more com-plicated. See the rw-FAQ and www.stats.uwo.ca/faculty/murdoch/software/debuggingR/

gdb.shtml.

4.4.2 Inspecting R objects when debugging

The key to inspecting R objects from compiled code is the function PrintValue(SEXP s) whichuses the normal R printing mechanisms to print the R object pointed to by s, or the safer versionR_PV(SEXP s) which will only print ‘objects’.

One way to make use of PrintValue is to insert suitable calls into the code to be debugged.

Another way is to call R_PV from the symbolic debugger. (PrintValue is hidden as Rf_

PrintValue.) For example, from gdb we can use

Chapter 4: Debugging 83

(gdb) p R_PV(ab)

using the object ab from the convolution example, if we have placed a suitable breakpoint inthe convolution C code.

To examine an arbitrary R object we need to work a little harder. For example, let

R> DF <- data.frame(a = 1:3, b = 4:6)

By setting a breakpoint at do_get and typing get("DF") at the R prompt, one can find out theaddress in memory of DF, for example

Value returned is $1 = (SEXPREC *) 0x40583e1c

(gdb) p *$1

$2 = {

sxpinfo = {type = 19, obj = 1, named = 1, gp = 0,

mark = 0, debug = 0, trace = 0, = 0},

attrib = 0x40583e80,

u = {

vecsxp = {

length = 2,

type = {c = 0x40634700 "0>X@D>X@0>X@", i = 0x40634700,

f = 0x40634700, z = 0x40634700, s = 0x40634700},

truelength = 1075851272,

},

primsxp = {offset = 2},

symsxp = {pname = 0x2, value = 0x40634700, internal = 0x40203008},

listsxp = {carval = 0x2, cdrval = 0x40634700, tagval = 0x40203008},

envsxp = {frame = 0x2, enclos = 0x40634700},

closxp = {formals = 0x2, body = 0x40634700, env = 0x40203008},

promsxp = {value = 0x2, expr = 0x40634700, env = 0x40203008}

}

}

(Debugger output reformatted for better legibility).

Using R_PV() one can “inspect” the values of the various elements of the SEXP, for example,

(gdb) p R_PV($1->attrib)

$names

[1] "a" "b"

$row.names

[1] "1" "2" "3"

$class

[1] "data.frame"

$3 = void

To find out where exactly the corresponding information is stored, one needs to go “deeper”:

Chapter 4: Debugging 84

(gdb) set $a = $1->attrib

(gdb) p $a->u.listsxp.tagval->u.symsxp.pname->u.vecsxp.type.c

$4 = 0x405d40e8 "names"

(gdb) p $a->u.listsxp.carval->u.vecsxp.type.s[1]->u.vecsxp.type.c

$5 = 0x40634378 "b"

(gdb) p $1->u.vecsxp.type.s[0]->u.vecsxp.type.i[0]

$6 = 1

(gdb) p $1->u.vecsxp.type.s[1]->u.vecsxp.type.i[1]

$7 = 5

Another alternative is the R_inspect function which shows the low-level structure of the ob-jects recursively (addresses differ from the above as this example is created on another machine):

(gdb) p R_inspect($1)

@100954d18 19 VECSXP g0c2 [OBJ,NAM(2),ATT] (len=2, tl=0)

@100954d50 13 INTSXP g0c2 [NAM(2)] (len=3, tl=0) 1,2,3

@100954d88 13 INTSXP g0c2 [NAM(2)] (len=3, tl=0) 4,5,6

ATTRIB:

@102a70140 02 LISTSXP g0c0 []

TAG: @10083c478 01 SYMSXP g0c0 [MARK,NAM(2),gp=0x4000] "names"

@100954dc0 16 STRSXP g0c2 [NAM(2)] (len=2, tl=0)

@10099df28 09 CHARSXP g0c1 [MARK,gp=0x21] "a"

@10095e518 09 CHARSXP g0c1 [MARK,gp=0x21] "b"

TAG: @100859e60 01 SYMSXP g0c0 [MARK,NAM(2),gp=0x4000] "row.names"

@102a6f868 13 INTSXP g0c1 [NAM(2)] (len=2, tl=1) -2147483648,-3

TAG: @10083c948 01 SYMSXP g0c0 [MARK,gp=0x4000] "class"

@102a6f838 16 STRSXP g0c1 [NAM(2)] (len=1, tl=1)

@1008c6d48 09 CHARSXP g0c2 [MARK,gp=0x21,ATT] "data.frame"

In general the representation of each object follows the format:@<address> <type-nr> <type-name> <gc-info> [<flags>] ...

For a more fine-grained control over the the depth of the recursion and the output of vectorsR_inspect3 takes additional two integer parameters: maximum depth and the maximal numberof elements that will be printed for scalar vectors. The defaults in R_inspect are currently -1(no limit) and 5 respectively.

Chapter 5: System and foreign language interfaces 85

5 System and foreign language interfaces

5.1 Operating system access

Access to operating system functions is via the R functions system and system2. The detailswill differ by platform (see the on-line help), and about all that can safely be assumed is thatthe first argument will be a string command that will be passed for execution (not necessarilyby a shell) and the second argument to system will be internal which if true will collect theoutput of the command into an R character vector.

On POSIX-compliant OSes these commands pass a command-line to a shell: Windows is notPOSIX-compliant and there is a separate function shell to do so.

The function system.time is available for timing. Timing on child processes is only availableon Unix-alikes, and may not be reliable there.

5.2 Interface functions .C and .Fortran

These two functions provide an interface to compiled code that has been linked into R, eitherat build time or via dyn.load (see Section 5.3 [dyn.load and dyn.unload], page 87). They areprimarily intended for compiled C and FORTRAN 77 code respectively, but the .C function canbe used with other languages which can generate C interfaces, for example C++ (see Section 5.6[Interfacing C++ code], page 93).

The first argument to each function is a character string specifying the symbol name asknown1 to C or FORTRAN, that is the function or subroutine name. (That the symbol isloaded can be tested by, for example, is.loaded("cg"). Use the name you pass to .C or.Fortran rather than the translated symbol name.)

There can be up to 65 further arguments giving R objects to be passed to compiled code.Normally these are copied before being passed in, and copied again to an R list object whenthe compiled code returns. If the arguments are given names, these are used as names for thecomponents in the returned list object (but not passed to the compiled code).

The following table gives the mapping between the modes of R atomic vectors and the typesof arguments to a C function or FORTRAN subroutine.

R storage mode C type FORTRAN typelogical int * INTEGER

integer int * INTEGER

double double * DOUBLE PRECISION

complex Rcomplex * DOUBLE COMPLEX

character char ** CHARACTER*255

raw unsigned char * none

Do please note the first two. On the 64-bit Unix/Linux/OS X platforms, long is 64-bitwhereas int and INTEGER are 32-bit. Code ported from S-PLUS (which uses long * for logicaland integer) will not work on all 64-bit platforms (although it may appear to work on some,including Windows). Note also that if your compiled code is a mixture of C functions andFORTRAN subprograms the argument types must match as given in the table above.

C type Rcomplex is a structure with double members r and i defined in the header file R_

ext/Complex.h included by R.h. (On most platforms this is stored in a way compatible with theC99 double complex type: however, it may not be possible to pass Rcomplex to a C99 functionexpecting a double complex argument. Nor need it be compatible with a C++ complex type.Moreover, the compatibility can depends on the optimization level set for the compiler.)

1 possibly after some platform-specific translation, e.g. adding leading or trailing underscores.

Chapter 5: System and foreign language interfaces 86

Only a single character string can be passed to or from FORTRAN, and the success of thisis compiler-dependent. Other R objects can be passed to .C, but it is much better to use one ofthe other interfaces.

It is possible to pass numeric vectors of storage mode double to C as float * or to FORTRANas REAL by setting the attribute Csingle, most conveniently by using the R functions as.single,single or mode. This is intended only to be used to aid interfacing existing C or FORTRANcode.

Logical values are sent as 0 (FALSE), 1 (TRUE) or INT_MIN = -2147483648 (NA, but only ifNAOK is true), and the compiled code should return one of these three values. (Non-zero valuesother than INT_MIN are mapped to TRUE.)

Unless formal argument NAOK is true, all the other arguments are checked for missing valuesNA and for the IEEE special values NaN, Inf and -Inf, and the presence of any of these generatesan error. If it is true, these values are passed unchecked.

Argument DUP can be used to suppress copying. It is dangerous: see the on-line help forarguments against its use. It is not possible to pass numeric vectors as float * or REAL if DUP= FALSE, and character vectors cannot be used.

Argument PACKAGE confines the search for the symbol name to a specific shared object (oruse "base" for code compiled into R). Its use is highly desirable, as there is no way to avoid twopackage writers using the same symbol name, and such name clashes are normally sufficient tocause R to crash. (If it is not present and the call is from the body of a function defined in apackage namespace, the shared object loaded by the first (if any) useDynLib directive will beused. However, prior to R 2.15.2 the detection of the correct namespace is unreliable and youare strongly recommended to use the PACKAGE argument for packages to be used with earlierversions of R.

Note that the compiled code should not return anything except through its arguments: Cfunctions should be of type void and FORTRAN subprograms should be subroutines.

To fix ideas, let us consider a very simple example which convolves two finite sequences.(This is hard to do fast in interpreted R code, but easy in C code.) We could do this using .C

by

void convolve(double *a, int *na, double *b, int *nb, double *ab)

{

int nab = *na + *nb - 1;

for(int i = 0; i < nab; i++)

ab[i] = 0.0;

for(int i = 0; i < *na; i++)

for(int j = 0; j < *nb; j++)

ab[i + j] += a[i] * b[j];

}

called from R by

conv <- function(a, b)

.C("convolve",

as.double(a),

as.integer(length(a)),

as.double(b),

as.integer(length(b)),

ab = double(length(a) + length(b) - 1))$ab

Note that we take care to coerce all the arguments to the correct R storage mode beforecalling .C; mistakes in matching the types can lead to wrong results or hard-to-catch errors.

Chapter 5: System and foreign language interfaces 87

Special care is needed in handling character vector arguments in C (or C++). Since onlyDUP = TRUE is allowed, on entry the contents of the elements are duplicated and assigned to theelements of a char ** array, and on exit the elements of the C array are copied to create newelements of a character vector. This means that the contents of the character strings of thechar ** array can be changed, including to \0 to shorten the string, but the strings cannot belengthened. It is possible2 to allocate a new string via R_alloc and replace an entry in the char** array by the new string. However, when character vectors are used other than in a read-onlyway, the .Call interface is much to be preferred.

Passing character strings to FORTRAN code needs even more care, and should be avoidedwhere possible. Only the first element of the character vector is passed in, as a fixed-length(255) character array. Up to 255 characters are passed back to a length-one character vector.How well this works (or even if it works at all) depends on the C and FORTRAN compilers oneach platform (including on their options). Often what is being passed to FORTRAN is one ofa small set of possible values (a factor in R terms) which could alternatively be passed as aninteger code: similarly FORTRAN code that wants to generate diagnostic messages can pass aninteger code to a C or R wrapper which will convert it to a character string.

It is possible to pass some R objects other than atomic vectors via .C, but this is onlysupported for historical compatibility: use the .Call or .External interfaces for such objects.Any C/C++ code that includes Rinternals.h should be called via .Call or .External.

5.3 dyn.load and dyn.unload

Compiled code to be used with R is loaded as a shared object (Unix-alikes including OS X, seeSection 5.5 [Creating shared objects], page 92 for more information) or DLL (Windows).

The shared object/DLL is loaded by dyn.load and unloaded by dyn.unload. Unloading isnot normally necessary, but it is needed to allow the DLL to be re-built on some platforms,including Windows.

The first argument to both functions is a character string giving the path to the object.Programmers should not assume a specific file extension for the object/DLL (such as .so) butuse a construction like

file.path(path1, path2, paste0("mylib", .Platform$dynlib.ext))

for platform independence. On Unix-alike systems the path supplied to dyn.load can be anabsolute path, one relative to the current directory or, if it starts with ‘~’, relative to the user’shome directory.

Loading is most often done automatically based on the useDynLib() declaration in theNAMESPACE file, but may be done explicitly via a call to library.dynam. This has the form

library.dynam("libname", package, lib.loc)

where libname is the object/DLL name with the extension omitted. Note that the first argument,chname, should not be package since this will not work if the package is installed under anothername.

Under some Unix-alike systems there is a choice of how the symbols are resolved when theobject is loaded, governed by the arguments local and now. Only use these if really neces-sary: in particular using now=FALSE and then calling an unresolved symbol will terminate Runceremoniously.

R provides a way of executing some code automatically when a object/DLL is either loadedor unloaded. This can be used, for example, to register native routines with R’s dynamic symbolmechanism, initialize some data in the native code, or initialize a third party library. On loadinga DLL, R will look for a routine within that DLL named R_init_lib where lib is the name ofthe DLL file with the extension removed. For example, in the command

2 Note that this is then not checked for over-runs by option CBoundsCheck = TRUE.

Chapter 5: System and foreign language interfaces 88

library.dynam("mylib", package, lib.loc)

R looks for the symbol named R_init_mylib. Similarly, when unloading the object, R looks fora routine named R_unload_lib, e.g., R_unload_mylib. In either case, if the routine is present,R will invoke it and pass it a single argument describing the DLL. This is a value of type DllInfowhich is defined in the Rdynload.h file in the R_ext directory.

Note that there are some implicit restrictions on this mechanism as the basename of the DLLneeds to be both a valid file name and valid as part of a C entry point (e.g. it cannot contain ‘.’):for portable code it is best to confine DLL names to be ASCII alphanumeric plus underscore. Ifentry point R_init_lib is not found it is also looked for with ‘.’ replaced by ‘_’.

The following example shows templates for the initialization and unload routines for themylib DLL.� �

#include <R.h>

#include <Rinternals.h>

#include <R_ext/Rdynload.h>

void

R_init_mylib(DllInfo *info)

{

/* Register routines,

allocate resources. */

}

void

R_unload_mylib(DllInfo *info)

{

/* Release resources. */

} If a shared object/DLL is loaded more than once the most recent version is used. More

generally, if the same symbol name appears in several shared objects, the most recently loadedoccurrence is used. The PACKAGE argument and registration (see the next section) provide goodways to avoid any ambiguity in which occurrence is meant.

On Unix-alikes the paths used to resolve dynamically linked dependent libraries are fixed (forsecurity reasons) when the process is launched, so dyn.load will only look for such libraries inthe locations set by the R shell script (via etc/ldpaths) and in the OS-specific defaults.

Windows allows more control (and less security) over where dependent DLLs are looked for.On all versions this includes the PATH environment variable, but with lowest priority: note thatit does not include the directory from which the DLL was loaded. It is possible to add a singlepath with quite high priority via the DLLpath argument to dyn.load. This is (by default) usedby library.dynam to include the package’s libs/i386 or libs/x64 directory in the DLL searchpath.

5.4 Registering native routines

By ‘native’ routine, we mean an entry point in compiled code.

In calls to .C, .Call, .Fortran and .External, R must locate the specified native routine bylooking in the appropriate shared object/DLL. By default, R uses the operating system-specificdynamic loader to lookup the symbol in all loaded DLLs and elsewhere. Alternatively, theauthor of the DLL can explicitly register routines with R and use a single, platform-independentmechanism for finding the routines in the DLL. One can use this registration mechanism to

Chapter 5: System and foreign language interfaces 89

provide additional information about a routine, including the number and type of the arguments,and also make it available to R programmers under a different name. In the future, registrationmay be used to implement a form of “secure” or limited native access.

To register routines with R, one calls the C routine R_registerRoutines. This is typicallydone when the DLL is first loaded within the initialization routine R_init_dll name described inSection 5.3 [dyn.load and dyn.unload], page 87. R_registerRoutines takes 5 arguments. Thefirst is the DllInfo object passed by R to the initialization routine. This is where R stores theinformation about the methods. The remaining 4 arguments are arrays describing the routinesfor each of the 4 different interfaces: .C, .Call, .Fortran and .External. Each argument is aNULL-terminated array of the element types given in the following table:

.C R_CMethodDef

.Call R_CallMethodDef

.Fortran R_FortranMethodDef

.External R_ExternalMethodDef

Currently, the R_ExternalMethodDef is the same as R_CallMethodDef type and containsfields for the name of the routine by which it can be accessed in R, a pointer to the actual nativesymbol (i.e., the routine itself), and the number of arguments the routine expects to be passedfrom R. For example, if we had a routine named myCall defined as

SEXP myCall(SEXP a, SEXP b, SEXP c);

we would describe this as

R_CallMethodDef callMethods[] = {

{"myCall", (DL_FUNC) &myCall, 3},

{NULL, NULL, 0}

};

along with any other routines for the .Call interface. For routines with a variable number ofarguments invoked via the .External interface, one specifies -1 for the number of argumentswhich tells R not to check the actual number passed. Note that the number of arguments passedto .External were not checked prior to R 3.0.0.

Routines for use with the .C and .Fortran interfaces are described with similar data struc-tures, but which have two additional fields for describing the type and “style” of each argument.Each of these can be omitted. However, if specified, each should be an array with the samenumber of elements as the number of parameters for the routine. The types array should con-tain the SEXP types describing the expected type of the argument. (Technically, the elementsof the types array are of type R_NativePrimitiveArgType which is just an unsigned integer.)The R types and corresponding type identifiers are provided in the following table:

numeric REALSXP

integer INTSXP

logical LGLSXP

single SINGLESXP

character STRSXP

list VECSXP

Consider a C routine, myC, declared as

void myC(double *x, int *n, char **names, int *status);

We would register it as

R_CMethodDef cMethods[] = {

{"myC", (DL_FUNC) &myC, 4, {REALSXP, INTSXP, STRSXP, LGLSXP}},

{NULL, NULL, 0}

};

Chapter 5: System and foreign language interfaces 90

One can also specify whether each argument is used simply as input, or as output, or as bothinput and output. The style field in the description of a method is used for this. The purpose isto allow3 R to transfer values more efficiently across the R-C/FORTRAN interface by avoidingcopying values when it is not necessary. Typically, one omits this information in the registrationdata.

Having created the arrays describing each routine, the last step is to actually register themwith R. We do this by calling R_registerRoutines. For example, if we have the descriptionsabove for the routines accessed by the .C and .Call we would use the following code:

void

R_init_myLib(DllInfo *info)

{

R_registerRoutines(info, cMethods, callMethods, NULL, NULL);

}

This routine will be invoked when R loads the shared object/DLL named myLib. The lasttwo arguments in the call to R_registerRoutines are for the routines accessed by .Fortran

and .External interfaces. In our example, these are given as NULL since we have no routines ofthese types.

When R unloads a shared object/DLL, its registrations are automatically removed. There isno other facility for unregistering a symbol.

Examples of registering routines can be found in the different packages in the R source tree(e.g., stats). Also, there is a brief, high-level introduction in R News (volume 1/3, September2001, pages 20–23, http://www.r-project.org/doc/Rnews/Rnews_2001-3.pdf).

Once routines are registered, they can be referred to as R objects if they this is arrangedin the useDynLib call in the package’s NAMESPACE file (see Section 1.5.4 [useDynLib], page 36).This avoids the overhead of looking up an entry point each time it is used, and ensure thatthe entry point in the package is the one used (without a PACKAGE = "pkg" argument). So forexample the stats package has

# Refer to all C/Fortran routines by their name prefixed by C_

useDynLib(stats, .registration = TRUE, .fixes = "C_")

in its NAMESPACE file, and then ansari.test’s default methods can contain

pansari <- function(q, m, n)

.C(C_pansari, as.integer(length(q)), p = as.double(q),

as.integer(m), as.integer(n))$p

5.4.1 Speed considerations

Sometimes registering native routines or using a PACKAGE argument can make a large difference.The results can depend quite markedly on the OS (and even if it is 32- or 64-bit), on the versionof R and what else is loaded into R at the time.

To fix ideas, first consider x84_64 OS 10.7 and R 2.15.2. A simple .Call function might be

foo <- function(x) .Call("foo", x)

with C code

SEXP foo(SEXP x)

{

return x;

}

If we compile with by R CMD SHLIB foo.c, load the code by dyn.load("foo.so") and runfoo(pi) it took around 22 microseconds (us). Specifying the DLL by

3 but this is not currently done.

Chapter 5: System and foreign language interfaces 91

foo2 <- function(x) .Call("foo", x, PACKAGE = "foo")

reduced the time to 1.7 us.

Now consider making these functions part of a package whose NAMESPACE file usesuseDynlib(foo). This immediately reduces the running time as "foo" will be preferentiallylooked for foo.dll. Without specifying PACKAGE it took about 5 us (it needs to fathom outthe appropriate DLL each time it is invoked but it does not need to search all DLLs), and withthe PACKAGE argument it is again about 1.7 us.

Next suppose the package has registered the native routine foo. Then foo() still has to findthe appropriate DLL but can get to the entry point in the DLL faster, in about 4.2 us. Andfoo2() now takes about 1 us. If we register the symbols in the NAMESPACE file and use

foo3 <- function(x) .Call(C_foo, x)

then the address for the native routine is looked up just once when the package is loaded,and foo3(pi) takes about 0.8 us.

Versions using .C() rather than .Call() take about 0.2 us longer.

These are all quite small differences, but C routines are not uncommonly invoked millions oftimes for run times of a few microseconds, and those doing such things may wish to be aware ofthe differences.

On Linux and Solaris there is a much smaller overhead in looking up symbols so foo(pi)

takes around 5 times as long as foo3(pi).

Symbol lookup on Windows used to be far slower, so R maintains a small cache. If the cacheis currently empty enough that the symbol can be stored in the cache then the performanceis similar to Linux and Solaris: if not it may be slower. R’s own code always uses registeredsymbols and so these never contribute to the cache: however many other packages do rely onsymbol lookup.

5.4.2 Linking to native routines in other packages

In addition to registering C routines to be called by R, it can at times be useful for one packageto make some of its C routines available to be called by C code in another package. The interfaceconsists of two routines declared in header R_ext/Rdynload.h as

void R_RegisterCCallable(const char *package, const char *name,

DL_FUNC fptr);

DL_FUNC R_GetCCallable(const char *package, const char *name);

A package packA that wants to make a C routine myCfun available to C code in other packageswould include the call

R_RegisterCCallable("packA", "myCfun", myCfun);

in its initialization function R_init_packA. A package packB that wants to use this routinewould retrieve the function pointer with a call of the form

p_myCfun = R_GetCCallable("packA", "myCfun");

The author of packB is responsible for ensuring that p_myCfun has an appropriate declaration.In the future R may provide some automated tools to simplify exporting larger numbers ofroutines.

A package that wishes to make use of header files in other packages needs to declare themas a comma-separated list in the field ‘LinkingTo’ in the DESCRIPTION file. This then arrangesthat the include directories in the installed linked-to packages are added to the include pathsfor C and C++ code.

It must also specify ‘Imports’4 or ‘Depends’ of those packages, for they have to be loadedprior to this one (so the path to their compiled code has been registered).

4 with a corresponding import or importFrom entry in the NAMESPACE file.

Chapter 5: System and foreign language interfaces 92

A CRAN example of the use of this mechanism is package lme4, which links to Matrix.

5.5 Creating shared objects

Shared objects for loading into R can be created using R CMD SHLIB. This accepts as argumentsa list of files which must be object files (with extension .o) or sources for C, C++, FORTRAN77, Fortran 9x, Objective C or Objective C++ (with extensions .c, .cc or .cpp, .f, .f90 or.f95, .m, and .mm or .M, respectively), or commands to be passed to the linker. See R CMD SHLIB

--help (or the R help for SHLIB) for usage information.

If compiling the source files does not work “out of the box”, you can specify additional flagsby setting some of the variables PKG_CPPFLAGS (for the C preprocessor, typically ‘-I’ flags), PKG_CFLAGS, PKG_CXXFLAGS, PKG_FFLAGS, PKG_FCFLAGS, PKG_OBJCFLAGS, and PKG_OBJCXXFLAGS (forthe C, C++, FORTRAN 77, Fortran 9x, Objective C, and Objective C++ compilers, respectively)in the file Makevars in the compilation directory (or, of course, create the object files directlyfrom the command line). Similarly, variable PKG_LIBS in Makevars can be used for additional‘-l’ and ‘-L’ flags to be passed to the linker when building the shared object. (Supplying linkercommands as arguments to R CMD SHLIB will take precedence over PKG_LIBS in Makevars.)

It is possible to arrange to include compiled code from other languages by setting the macro‘OBJECTS’ in file Makevars, together with suitable rules to make the objects.

Flags which are already set (for example in file etcR_ARCH/Makeconf) can be overridden bythe environment variable MAKEFLAGS (at least for systems using a POSIX-compliant make), asin (Bourne shell syntax)

MAKEFLAGS="CFLAGS=-O3" R CMD SHLIB *.c

It is also possible to set such variables in personal Makevars files, which are read after thelocal Makevars and the system makefiles or in a site-wide Makevars.site file. See Section“Customizing package compilation” in R Installation and Administration,

Note that as R CMD SHLIB uses Make, it will not remake a shared object just because the flagshave changed, and if test.c and test.f both exist in the current directory

R CMD SHLIB test.f

will compile test.c!

If the src subdirectory of an add-on package contains source code with one of the extensionslisted above or a file Makevars but not a file Makefile, R CMD INSTALL creates a shared object(for loading into R through useDynlib in the NAMESPACE, or in the .onLoad function of thepackage) using the R CMD SHLIB mechanism. If file Makevars exists it is read first, then thesystem makefile and then any personal Makevars files.

If the src subdirectory of package contains a file Makefile, this is used by R CMD

INSTALL in place of the R CMD SHLIB mechanism. make is called with makefiles R_HOME/etcR_ARCH/Makeconf, src/Makefile and any personal Makevars files (in that order). The firsttarget found in src/Makefile is used.

It is better to make use of a Makevars file rather than a Makefile: the latter should beneeded only exceptionally.

Under Windows the same commands work, but Makevars.win will be used in preferenceto Makevars, and only src/Makefile.win will be used by R CMD INSTALL with src/Makefile

being ignored. For past experiences of building DLLs with a variety of compilers, seefile ‘README.packages’ and http: / /www .stats .uwo .ca /faculty /murdoch /software /

compilingDLLs / . Under Windows you can supply an exports definitions file calleddllname-win.def: otherwise all entry points in objects (but not libraries) supplied to R CMD

SHLIB will be exported from the DLL. An example is stats-win.def for the stats package: aCRAN example in package fastICA.

Chapter 5: System and foreign language interfaces 93

If you feel tempted to read the source code and subvert these mechanisms, please resist. Fartoo much developer time has been wasted in chasing down errors caused by failures to followthis documentation, and even more by package authors demanding explanations as to why theirpackages not longer work. In particular, undocumented environment or make variables are notfor use by package writers and are subject to change without notice.

5.6 Interfacing C++ code

Suppose we have the following hypothetical C++ library, consisting of the two files X.h andX.cpp, and implementing the two classes X and Y which we want to use in R.� �

// X.h

class X {

public: X (); ~X ();

};

class Y {

public: Y (); ~Y ();

}; � �// X.cpp

#include <R.h>

#include "X.h"

static Y y;

X::X() { REprintf("constructor X\n"); }

X::~X() { REprintf("destructor X\n"); }

Y::Y() { REprintf("constructor Y\n"); }

Y::~Y() { REprintf("destructor Y\n"); }

To use with R, the only thing we have to do is writing a wrapper function and ensuring thatthe function is enclosed in

extern "C" {

}

For example,

Chapter 5: System and foreign language interfaces 94� �// X_main.cpp:

#include "X.h"

extern "C" {

void X_main () {

X x;

}

} // extern "C" Compiling and linking should be done with the C++ compiler-linker (rather than the C

compiler-linker or the linker itself); otherwise, the C++ initialization code (and hence the con-structor of the static variable Y) are not called. On a properly configured system, one can simplyuse

R CMD SHLIB X.cpp X_main.cpp

to create the shared object, typically X.so (the file name extension may be different on yourplatform). Now starting R yields

R version 2.14.1 Patched (2012-01-16 r58124)

Copyright (C) 2012 The R Foundation for Statistical Computing

...

Type "q()" to quit R.

R> dyn.load(paste("X", .Platform$dynlib.ext, sep = ""))

constructor Y

R> .C("X_main")

constructor X

destructor X

list()

R> q()

Save workspace image? [y/n/c]: y

destructor Y

The R for Windows FAQ (rw-FAQ) contains details of how to compile this example underWindows.

Earlier version of this example used C++ iostreams: this is best avoided. There is no guaranteethat the output will appear in the R console, and indeed it will not on the R for Windows console.Use R code or the C entry points (see Section 6.5 [Printing], page 122) for all I/O if at all possible.Examples have been seen where merely loading a DLL that contained calls to C++ I/O upsetR’s own C I/O (for example by resetting buffers on open files).

Most R header files can be included within C++ programs, and they should not be includedwithin an extern "C" block (as they include C++ system headers). It may not be possible toinclude some R headers as they in turn include C header files that may cause conflicts—if thishappens, define ‘NO_C_HEADERS’ before including the R headers, and include C++ versions (suchas ‘cmath’) of the appropriate headers yourself before the R headers.

5.7 Fortran I/O

We have already warned against the use of C++ iostreams not least because output is notguaranteed to appear on the R console, and this warning applies equally to Fortran (77 or 9x)output to units * and 6. See Section 6.5.1 [Printing from FORTRAN], page 122, which describesworkarounds.

Chapter 5: System and foreign language interfaces 95

In the past most Fortran compilers implemented I/O on top of the C I/O system and so thetwo interworked successfully. This was true of g77, but it is less true of gfortran as used ingcc 4.y.z. In particular, any package that makes use of Fortran I/O will when compiled onWindows interfere with C I/O: when the Fortran I/O is initialized (typically when the packageis loaded) the C stdout and stderr are switched to LF line endings. (Function init in filesrc/modules/lapack/init_win.c shows how to mitigate this.)

5.8 Linking to other packages

It is not in general possible to link a DLL in package packA to a DLL provided by packagepackB (for the security reasons mentioned in Section 5.3 [dyn.load and dyn.unload], page 87,and also because some platforms distinguish between shared objects and dynamic libraries), butit is on Windows.

Note that there can be tricky versioning issues here, as package packB could be re-installed af-ter package packA — it is desirable that the API provided by package packB remains backwards-compatible.

5.8.1 Unix-alikes

It is possible to link a shared object in package packA to a library provided by package packBunder limited circumstances on a Unix-alike OS. There are severe portability issues, so this isnot recommended for a distributed package.

This is easiest if packB provides a static library packB/libs/libpackB.a. (This will needto be compiled with PIC flags on platforms where it matters.) Then as the code from packagepackB is incorporated when package packA is installed, we only need to find the static libraryat install time for package packB. The only issue is to find package packB, and for that we canask R by something like (long lines broken for display here)

PKGB_PATH=‘echo ’library(packB);

cat(system.file("libs", package="packB", mustWork=TRUE))’ \

| "${R_HOME}/bin/R" --vanilla --slave‘

PKG_LIBS="$(PKGB_PATH)$(R_ARCH)/libpackB.a"

For a dynamic library packB/libs/libpackB.so (packB/libs/libpackB.dylib on OS X:note that you cannot link to a shared object, .so on that platform) we could use

PKGB_PATH=‘echo ’library(packB);

cat(system.file("libs", package="packB", mustWork=TRUE))’ \

| "${R_HOME}/bin/R" --vanilla --slave‘

PKG_LIBS=-L"$(PKGB_PATH)$(R_ARCH)" -lpackB

This will work for installation, but very likely not when package packB is loaded, as the pathto package packB’s libs directory is not in the ld.so5 search path. You can arrange to put itthere before R is launched by setting (on some platforms) LD_RUN_PATH or LD_LIBRARY_PATH oradding to the ld.so cache (see man ldconfig). On platforms that support it, the path to thedynamic library can be hardcoded at install time (which assumes that the location of packagepackB will not be changed) nor the package updated to a changed API). On systems with theGNU linker (e.g. Linux) and some others (e.g. OS X) this can be done by e.g.

PKGB_PATH=‘echo ’library(packB);

cat(system.file("libs", package="packB", mustWork=TRUE)))’ \

| "${R_HOME}/bin/R" --vanilla --slave‘

PKG_LIBS=-L"$(PKGB_PATH)$(R_ARCH)" -Wl,-rpath -Wl,"$(PKGB_PATH)$(R_ARCH)" -lpackB

and on some other systems (e.g. Solaris with its native linker) use -Rdir rather than -rpath

dir (and this is accepted by the compiler as well as the linker).

5 dyld on OS X, and DYLD_LIBRARY_PATHS below.

Chapter 5: System and foreign language interfaces 96

It may be possible to figure out what is required semi-automatically from the result of R CMD

libtool --config (look for ‘hardcode’), although that does not currently know the spell forOS X (as given in the example above).

Making headers provided by package packB available to the code to be compiled in packagepackA can be done by the LinkingTo mechanism (see Section 5.4 [Registering native routines],page 88).

5.8.2 Windows

Suppose package packA wants to make use of compiled code provided by packB in DLLpackB/libs/exB.dll, possibly the package’s DLL packB/libs/packB.dll. (This can be ex-tended to linking to more than one package in a similar way.) There are three issues to beaddressed:

• Making headers provided by package packB available to the code to be compiled in packagepackA.

This is done by the LinkingTo mechanism (see Section 5.4 [Registering native routines],page 88).

• preparing packA.dll to link to packB/libs/exB.dll.

This needs an entry in Makevars.win of the form

PKG_LIBS= -L<something> -lexB

and one possibility is that <something> is the path to the installed pkgB/libs directory.To find that we need to ask R where it is by something like

PKGB_PATH=‘echo ’library(packB);

cat(system.file("libs", package="packB", mustWork=TRUE))’ \

| rterm --vanilla --slave‘

PKG_LIBS= -L"$(PKGB_PATH)$(R_ARCH)" -lexB

Another possibility is to use an import library, shipping with package packA an exports fileexB.def. Then Makevars.win could contain

PKG_LIBS= -L. -lexB

all: $(SHLIB) before

before: libexB.dll.a

libexB.dll.a: exB.def

and then installing package packA will make and use the import library for exB.dll. (Oneway to prepare the exports file is to use pexports.exe.)

• loading packA.dll which depends on exB.dll.

If exB.dll was used by package packB (because it is in fact packB.dll or packB.dll

depends on it) and packB has been loaded before packA, then nothing more needs to bedone as exB.dll will already be loaded into the R executable. (This is the most commonscenario.)

More generally, we can use the DLLpath argument to library.dynam to ensure that exB.dllis found, for example by setting

library.dynam("packA", pkg, lib,

DLLpath = system.file("libs", package="packB"))

Note that DLLpath can only set one path, and so for linking to two or more packages youwould need to resort to setting environment variable PATH.

Chapter 5: System and foreign language interfaces 97

5.9 Handling R objects in C

Using C code to speed up the execution of an R function is often very fruitful. Traditionally thishas been done via the .C function in R. However, if a user wants to write C code using internalR data structures, then that can be done using the .Call and .External functions. The syntaxfor the calling function in R in each case is similar to that of .C, but the two functions havedifferent C interfaces. Generally the .Call interface is simpler to use, but .External is a littlemore general.

A call to .Call is very similar to .C, for example

.Call("convolve2", a, b)

The first argument should be a character string giving a C symbol name of code that has alreadybeen loaded into R. Up to 65 R objects can passed as arguments. The C side of the interface is

#include <R.h>

#include <Rinternals.h>

SEXP convolve2(SEXP a, SEXP b)

...

A call to .External is almost identical

.External("convolveE", a, b)

but the C side of the interface is different, having only one argument

#include <R.h>

#include <Rinternals.h>

SEXP convolveE(SEXP args)

...

Here args is a LISTSXP, a Lisp-style pairlist from which the arguments can be extracted.

In each case the R objects are available for manipulation via a set of functions and macrosdefined in the header file Rinternals.h or some S-compatibility macros6 defined in Rdefines.h.See Section 5.10 [Interface functions .Call and .External], page 106 for details on .Call and.External.

Before you decide to use .Call or .External, you should look at other alternatives. First,consider working in interpreted R code; if this is fast enough, this is normally the best option.You should also see if using .C is enough. If the task to be performed in C is simple enoughinvolving only atomic vectors and requiring no call to R, .C suffices. A great deal of usefulcode was written using just .C before .Call and .External were available. These interfacesallow much more control, but they also impose much greater responsibilities so need to be usedwith care. Neither .Call nor .External copy their arguments: you should treat arguments youreceive through these interfaces as read-only.

To handle R objects from within C code we use the macros and functions that have beenused to implement the core parts of R. A public7 subset of these is defined in the headerfile Rinternals.h in the directory R_INCLUDE_DIR (default R_HOME/include) that should beavailable on any R installation.

A substantial amount of R, including the standard packages, is implemented using the func-tions and macros described here, so the R source code provides a rich source of examples and“how to do it”: do make use of the source code for inspirational examples.

6 That is, similar to those defined in S version 4 from the 1990s: these are not kept up to date and are notrecommended for new projects.

7 see Chapter 6 [The R API], page 119: note that these are not all part of the API.

Chapter 5: System and foreign language interfaces 98

It is necessary to know something about how R objects are handled in C code. All the Robjects you will deal with will be handled with the type SEXP8, which is a pointer to a structurewith typedef SEXPREC. Think of this structure as a variant type that can handle all the usualtypes of R objects, that is vectors of various modes, functions, environments, language objectsand so on. The details are given later in this section and in Section “R Internal Structures” inR Internals, but for most purposes the programmer does not need to know them. Think ratherof a model such as that used by Visual Basic, in which R objects are handed around in C code(as they are in interpreted R code) as the variant type, and the appropriate part is extractedfor, for example, numerical calculations, only when it is needed. As in interpreted R code, muchuse is made of coercion to force the variant object to the right type.

5.9.1 Handling the effects of garbage collection

We need to know a little about the way R handles memory allocation. The memory allocated forR objects is not freed by the user; instead, the memory is from time to time garbage collected.That is, some or all of the allocated memory not being used is freed or marked as re-usable.

The R object types are represented by a C structure defined by a typedef SEXPREC inRinternals.h. It contains several things among which are pointers to data blocks and toother SEXPRECs. A SEXP is simply a pointer to a SEXPREC.

If you create an R object in your C code, you must tell R that you are using the object byusing the PROTECT macro on a pointer to the object. This tells R that the object is in use so itis not destroyed during garbage collection. Notice that it is the object which is protected, notthe pointer variable. It is a common mistake to believe that if you invoked PROTECT(p) at somepoint then p is protected from then on, but that is not true once a new object is assigned to p.

Protecting an R object automatically protects all the R objects pointed to in the correspond-ing SEXPREC, for example all elements of a protected list are automatically protected.

The programmer is solely responsible for housekeeping the calls to PROTECT. There is acorresponding macro UNPROTECT that takes as argument an int giving the number of objectsto unprotect when they are no longer needed. The protection mechanism is stack-based, soUNPROTECT(n) unprotects the last n objects which were protected. The calls to PROTECT andUNPROTECT must balance when the user’s code returns. R will warn about "stack imbalance

in .Call" (or .External) if the housekeeping is wrong.

Here is a small example of creating an R numeric vector in C code:

#include <R.h>

#include <Rinternals.h>

SEXP ab;

....

ab = PROTECT(allocVector(REALSXP, 2));

REAL(ab)[0] = 123.45;

REAL(ab)[1] = 67.89;

UNPROTECT(1);

Now, the reader may ask how the R object could possibly get removed during those manipu-lations, as it is just our C code that is running. As it happens, we can do without the protectionin this example, but in general we do not know (nor want to know) what is hiding behind theR macros and functions we use, and any of them might cause memory to be allocated, hencegarbage collection and hence our object ab to be removed. It is usually wise to err on the sideof caution and assume that any of the R macros and functions might remove the object.

In some cases it is necessary to keep better track of whether protection is really needed. Beparticularly aware of situations where a large number of objects are generated. The pointer

8 SEXP is an acronym for S imple EXPression, common in LISP-like language syntaxes.

Chapter 5: System and foreign language interfaces 99

protection stack has a fixed size (default 10,000) and can become full. It is not a good ideathen to just PROTECT everything in sight and UNPROTECT several thousand objects at the end. Itwill almost invariably be possible to either assign the objects as part of another object (whichautomatically protects them) or unprotect them immediately after use.

Protection is not needed for objects which R already knows are in use. In particular, thisapplies to function arguments.

There is a less-used macro UNPROTECT_PTR(s) that unprotects the object pointed to by theSEXP s, even if it is not the top item on the pointer protection stack. This is rarely neededoutside the parser (the R sources currently have three examples, one in src/main/plot3d.c).

Sometimes an object is changed (for example duplicated, coerced or grown) yet the currentvalue needs to be protected. For these cases PROTECT_WITH_INDEX saves an index of the pro-tection location that can be used to replace the protected value using REPROTECT. For example(from the internal code for optim)

PROTECT_INDEX ipx;

....

s = PROTECT_WITH_INDEX(eval(OS->R_fcall, OS->R_env), &ipx);

s = REPROTECT(coerceVector(s, REALSXP), ipx);

Note that it is dangerous to mix UNPROTECT_PTR with PROTECT_WITH_INDEX, as the formerchanges the protection locations of objects that were protected after the one being unprotected.

5.9.2 Allocating storage

For many purposes it is sufficient to allocate R objects and manipulate those. There are quitea few allocXxx functions defined in Rinternals.h—you may want to explore them.

One that is commonly used is allocVector, the C-level equivalent of R-level vector() and itswrappers such as integer() and character(). One distinction is that whereas the R functionsalways initialize the elements of the vector, allocVector only does so for lists, expressions andcharacter vectors (the cases where the elements are themselves R objects).

If storage is required for C objects during the calculations this is best allocating by callingR_alloc; see Section 6.1 [Memory allocation], page 119. All of these memory allocation routinesdo their own error-checking, so the programmer may assume that they will raise an error andnot return if the memory cannot be allocated.

5.9.3 Details of R types

Users of the Rinternals.h macros will need to know how the R types are known internally. Thedifferent R data types are represented in C by SEXPTYPE. Some of these are familiar from Rand some are internal data types. The usual R object modes are given in the table.

SEXPTYPE R equivalentREALSXP numeric with storage mode doubleINTSXP integerCPLXSXP complexLGLSXP logicalSTRSXP characterVECSXP list (generic vector)LISTSXP pairlistDOTSXP a ‘...’ objectNILSXP NULLSYMSXP name/symbolCLOSXP function or function closure

Chapter 5: System and foreign language interfaces 100

ENVSXP environment

Among the important internal SEXPTYPEs are LANGSXP, CHARSXP, PROMSXP, etc. (Note: althoughit is possible to return objects of internal types, it is unsafe to do so as assumptions are madeabout how they are handled which may be violated at user-level evaluation.) More details aregiven in Section “R Internal Structures” in R Internals.

Unless you are very sure about the type of the arguments, the code should check the datatypes. Sometimes it may also be necessary to check data types of objects created by evaluatingan R expression in the C code. You can use functions like isReal, isInteger and isString todo type checking. See the header file Rinternals.h for definitions of other such functions. Allof these take a SEXP as argument and return 1 or 0 to indicate TRUE or FALSE.

What happens if the SEXP is not of the correct type? Sometimes you have no other optionexcept to generate an error. You can use the function error for this. It is usually better tocoerce the object to the correct type. For example, if you find that an SEXP is of the typeINTEGER, but you need a REAL object, you can change the type by using

newSexp = PROTECT(coerceVector(oldSexp, REALSXP));

Protection is needed as a new object is created; the object formerly pointed to by the SEXP isstill protected but now unused.9

All the coercion functions do their own error-checking, and generate NAs with a warning orstop with an error as appropriate.

Note that these coercion functions are not the same as calling as.numeric (and so on) in Rcode, as they do not dispatch on the class of the object. Thus it is normally preferable to dothe coercion in the calling R code.

So far we have only seen how to create and coerce R objects from C code, and how to extractthe numeric data from numeric R vectors. These can suffice to take us a long way in interfacingR objects to numerical algorithms, but we may need to know a little more to create useful returnobjects.

5.9.4 Attributes

Many R objects have attributes: some of the most useful are classes and the dim and dimnames

that mark objects as matrices or arrays. It can also be helpful to work with the names attributeof vectors.

To illustrate this, let us write code to take the outer product of two vectors (which outer

and %o% already do). As usual the R code is simple

out <- function(x, y)

{

storage.mode(x) <- storage.mode(y) <- "double"

.Call("out", x, y)

}

where we expect x and y to be numeric vectors (possibly integer), possibly with names. Thistime we do the coercion in the calling R code.

C code to do the computations is

9 If no coercion was required, coerceVector would have passed the old object through unchanged.

Chapter 5: System and foreign language interfaces 101

#include <R.h>

#include <Rinternals.h>

SEXP out(SEXP x, SEXP y)

{

int nx = length(x), ny = length(y);

SEXP ans = PROTECT(allocMatrix(REALSXP, nx, ny));

double *rx = REAL(x), *ry = REAL(y), *rans = REAL(ans);

for(int i = 0; i < nx; i++) {

double tmp = rx[i];

for(int j = 0; j < ny; j++)

rans[i + nx*j] = tmp * ry[j];

}

UNPROTECT(1);

return ans;

}

Note the way REAL is used: as it is a function call it can be considerably faster to store the resultand index that.

However, we would like to set the dimnames of the result. We can use

#include <R.h>

#include <Rinternals.h>

SEXP out(SEXP x, SEXP y)

{

int nx = length(x), ny = length(y);

SEXP ans = PROTECT(allocMatrix(REALSXP, nx, ny));

double *rx = REAL(x), *ry = REAL(y), *rans = REAL(ans);

for(int i = 0; i < nx; i++) {

double tmp = rx[i];

for(int j = 0; j < ny; j++)

rans[i + nx*j] = tmp * ry[j];

}

SEXP dimnames = PROTECT(allocVector(VECSXP, 2));

SET_VECTOR_ELT(dimnames, 0, getAttrib(x, R_NamesSymbol));

SET_VECTOR_ELT(dimnames, 1, getAttrib(y, R_NamesSymbol));

setAttrib(ans, R_DimNamesSymbol, dimnames);

UNPROTECT(3);

return ans;

}

This example introduces several new features. The getAttrib and setAttrib functions getand set individual attributes. Their second argument is a SEXP defining the name in the symboltable of the attribute we want; these and many such symbols are defined in the header fileRinternals.h.

There are shortcuts here too: the functions namesgets, dimgets and dimnamesgets are theinternal versions of the default methods of names<-, dim<- and dimnames<- (for vectors andarrays), and there are functions such as GetMatrixDimnames and GetArrayDimnames.

Chapter 5: System and foreign language interfaces 102

What happens if we want to add an attribute that is not pre-defined? We need to add asymbol for it via a call to install. Suppose for illustration we wanted to add an attribute"version" with value 3.0. We could use

SEXP version;

version = PROTECT(allocVector(REALSXP, 1));

REAL(version)[0] = 3.0;

setAttrib(ans, install("version"), version);

UNPROTECT(1);

Using install when it is not needed is harmless and provides a simple way to retrieve thesymbol from the symbol table if it is already installed. However, the lookup takes a non-trivialamount of time, so consider code such as

static SEXP VerSymbol = NULL;

...

if (VerSymbol == NULL) VerSymbol = install("version");

if it is to be done frequently.

This example can be simplified by another convenience function:

SEXP version = PROTECT(ScalarReal(3.0));

setAttrib(ans, install("version"), version);

UNPROTECT(1);

5.9.5 Classes

In R the class is just the attribute named "class" so it can be handled as such, but there is ashortcut classgets. Suppose we want to give the return value in our example the class "mat".We can use

#include <R.h>

#include <Rinternals.h>

....

SEXP ans, dim, dimnames, class;

....

class = PROTECT(allocVector(STRSXP, 1));

SET_STRING_ELT(class, 0, mkChar("mat"));

classgets(ans, class);

UNPROTECT(4);

return ans;

}

As the value is a character vector, we have to know how to create that from a C character array,which we do using the function mkChar.

5.9.6 Handling lists

Some care is needed with lists, as R moved early on from using LISP-like lists (now called“pairlists”) to S-like generic vectors. As a result, the appropriate test for an object of modelist is isNewList, and we need allocVector(VECSXP, n) and not allocList(n).

List elements can be retrieved or set by direct access to the elements of the generic vector.Suppose we have a list object

a <- list(f = 1, g = 2, h = 3)

Then we can access a$g as a[[2]] by

double g;

....

g = REAL(VECTOR_ELT(a, 1))[0];

Chapter 5: System and foreign language interfaces 103

This can rapidly become tedious, and the following function (based on one in package stats)is very useful:

/* get the list element named str, or return NULL */

SEXP getListElement(SEXP list, const char *str)

{

SEXP elmt = R_NilValue, names = getAttrib(list, R_NamesSymbol);

for (int i = 0; i < length(list); i++)

if(strcmp(CHAR(STRING_ELT(names, i)), str) == 0) {

elmt = VECTOR_ELT(list, i);

break;

}

return elmt;

}

and enables us to say

double g;

g = REAL(getListElement(a, "g"))[0];

5.9.7 Handling character data

R character vectors are stored as STRSXPs, a vector type like VECSXP where every element isof type CHARSXP. The CHARSXP elements of STRSXPs are accessed using STRING_ELT and SET_

STRING_ELT.

CHARSXPs are read-only objects and must never be modified. In particular, the C-style stringcontained in a CHARSXP should be treated as read-only and for this reason the CHAR function usedto access the character data of a CHARSXP returns (const char *) (this also allows compilers toissue warnings about improper use). Since CHARSXPs are immutable, the same CHARSXP can beshared by any STRSXP needing an element representing the same string. R maintains a globalcache of CHARSXPs so that there is only ever one CHARSXP representing a given string in memory.

You can obtain a CHARSXP by calling mkChar and providing a nul-terminated C-style string.This function will return a pre-existing CHARSXP if one with a matching string already exists,otherwise it will create a new one and add it to the cache before returning it to you. The variantmkCharLen can be used to create a CHARSXP from part of a buffer and will ensure null-termination.

Note that R character strings are restricted to 2^31 - 1 bytes, and hence so should the inputto mkChar be (C allows longer strings on 64-bit platforms).

5.9.8 Finding and setting variables

It will be usual that all the R objects needed in our C computations are passed as arguments to.Call or .External, but it is possible to find the values of R objects from within the C giventheir names. The following code is the equivalent of get(name, envir = rho).

Chapter 5: System and foreign language interfaces 104

SEXP getvar(SEXP name, SEXP rho)

{

SEXP ans;

if(!isString(name) || length(name) != 1)

error("name is not a single string");

if(!isEnvironment(rho))

error("rho should be an environment");

ans = findVar(install(CHAR(STRING_ELT(name, 0))), rho);

Rprintf("first value is %f\n", REAL(ans)[0]);

return R_NilValue;

}

The main work is done by findVar, but to use it we need to install name as a name in thesymbol table. As we wanted the value for internal use, we return NULL.

Similar functions with syntax

void defineVar(SEXP symbol, SEXP value, SEXP rho)

void setVar(SEXP symbol, SEXP value, SEXP rho)

can be used to assign values to R variables. defineVar creates a new binding or changes the valueof an existing binding in the specified environment frame; it is the analogue of assign(symbol,value, envir = rho, inherits = FALSE), but unlike assign, defineVar does not make a copyof the object value.10 setVar searches for an existing binding for symbol in rho or its enclosingenvironments. If a binding is found, its value is changed to value. Otherwise, a new binding withthe specified value is created in the global environment. This corresponds to assign(symbol,

value, envir = rho, inherits = TRUE).

5.9.9 Some convenience functions

Some operations are done so frequently that there are convenience functions to handle them.(All these are provided via the header file Rinternals.h.)

Suppose we wanted to pass a single logical argument ignore_quotes: we could use

int ign = asLogical(ignore_quotes);

if(ign == NA_LOGICAL) error("’ignore_quotes’ must be TRUE or FALSE");

which will do any coercion needed (at least from a vector argument), and return NA_LOGICAL ifthe value passed was NA or coercion failed. There are also asInteger, asReal and asComplex.The function asChar returns a CHARSXP. All of these functions ignore any elements of an inputvector after the first.

To return a length-one real vector we can use

double x;

...

return ScalarReal(x);

and there are versions of this for all the atomic vector types (those for a length-one charactervector being ScalarString with argument a CHARSXP and mkString with argument const char

*).

Some of the isXXXX functions differ from their apparent R-level counterparts: for exampleisVector is true for any atomic vector type (isVectorAtomic) and for lists and expressions(isVectorList) (with no check on attributes). isMatrix is a test of a length-2 "dim" attribute.

10 You can assign a copy of the object in the environment frame rho using defineVar(symbol,

duplicate(value), rho)).

Chapter 5: System and foreign language interfaces 105

There are a series of small macros/functions to help construct pairlists and language objects(whose internal structures just differ by SEXPTYPE). Function CONS(u, v) is the basic buildingblock: it constructs a pairlist from u followed by v (which is a pairlist or R_NilValue). LCONS isa variant that constructs a language object. Functions list1 to list5 construct a pairlist fromone to five items, and lang1 to lang6 do the same for a language object (a function to call pluszero to five arguments). Functions elt and lastElt find the ith element and the last elementof a pairlist, and nthcdr returns a pointer to the nth position in the pairlist (whose CAR is thenth item).

Functions str2type and type2str map R length-one character strings to and from SEXPTYPE

numbers, and type2char maps numbers to C character strings.

5.9.9.1 Semi-internal convenience functions

There is quite a collection of functions that may be used in your C code if you are willing toadapt to rare “API” changes. These typically contain “workhorses” of their R counterparts.

Functions any_duplicated and any_duplicated3 are fast versions of R’sany(duplicated(.)).

Function R_compute_identical corresponds to R’s identical function.

5.9.10 Named objects and copying

When assignments are done in R such as

x <- 1:10

y <- x

the named object is not necessarily copied, so after those two assignments y and x are bound tothe same SEXPREC (the structure a SEXP points to). This means that any code which alters one ofthem has to make a copy before modifying the copy if the usual R semantics are to apply. Notethat whereas .C and .Fortran do copy their arguments (unless the dangerous dup = FALSE isused), .Call and .External do not. So duplicate is commonly called on arguments to .Call

before modifying them.

However, at least some of this copying is unneeded. In the first assignment shown, x <- 1:10,R first creates an object with value 1:10 and then assigns it to x but if x is modified no copy isnecessary as the temporary object with value 1:10 cannot be referred to again. R distinguishesbetween named and unnamed objects via a field in a SEXPREC that can be accessed via themacros NAMED and SET_NAMED. This can take values

0 The object is not bound to any symbol

1 The object has been bound to exactly one symbol

2 The object has potentially been bound to two or more symbols, and one should actas if another variable is currently bound to this value.

Note the past tenses: R does not do full reference counting and there may currently be fewerbindings.

It is safe to modify the value of any SEXP for which NAMED(foo) is zero, and if NAMED(foo) istwo, the value should be duplicated (via a call to duplicate) before any modification. Note thatit is the responsibility of the author of the code making the modification to do the duplication,even if it is x whose value is being modified after y <- x.

The case NAMED(foo) == 1 allows some optimization, but it can be ignored (and duplicationdone whenever NAMED(foo) > 0). (This optimization is not currently usable in user code.) It isintended for use within replacement functions. Suppose we used

x <- 1:10

foo(x) <- 3

Chapter 5: System and foreign language interfaces 106

which is computed as

x <- 1:10

x <- "foo<-"(x, 3)

Then inside "foo<-" the object pointing to the current value of x will have NAMED(foo) as one,and it would be safe to modify it as the only symbol bound to it is x and that will be reboundimmediately. (Provided the remaining code in "foo<-" make no reference to x, and no one isgoing to attempt a direct call such as y <- "foo<-"(x).)

Currently all arguments to a .Call call will have NAMED set to 2, and so users must assumethat they need to be duplicated before alteration.

5.10 Interface functions .Call and .External

In this section we consider the details of the R/C interfaces.

These two interfaces have almost the same functionality. .Call is based on the interface ofthe same name in S version 4, and .External is based on R’s .Internal. .External is morecomplex but allows a variable number of arguments.

5.10.1 Calling .Call

Let us convert our finite convolution example to use .Call. The calling function in R is

conv <- function(a, b) .Call("convolve2", a, b)

which could hardly be simpler, but as we shall see all the type coercion is transferred to the Ccode, which is

#include <R.h>

#include <Rinternals.h>

SEXP convolve2(SEXP a, SEXP b)

{

int na, nb, nab;

double *xa, *xb, *xab;

SEXP ab;

a = PROTECT(coerceVector(a, REALSXP));

b = PROTECT(coerceVector(b, REALSXP));

na = length(a); nb = length(b); nab = na + nb - 1;

ab = PROTECT(allocVector(REALSXP, nab));

xa = REAL(a); xb = REAL(b); xab = REAL(ab);

for(int i = 0; i < nab; i++) xab[i] = 0.0;

for(int i = 0; i < na; i++)

for(int j = 0; j < nb; j++) xab[i + j] += xa[i] * xb[j];

UNPROTECT(3);

return ab;

}

5.10.2 Calling .External

We can use the same example to illustrate .External. The R code changes only by replacing.Call by .External

conv <- function(a, b) .External("convolveE", a, b)

but the main change is how the arguments are passed to the C code, this time as a single SEXP.The only change to the C code is how we handle the arguments.

Chapter 5: System and foreign language interfaces 107

#include <R.h>

#include <Rinternals.h>

SEXP convolveE(SEXP args)

{

int i, j, na, nb, nab;

double *xa, *xb, *xab;

SEXP a, b, ab;

a = PROTECT(coerceVector(CADR(args), REALSXP));

b = PROTECT(coerceVector(CADDR(args), REALSXP));

...

}

Once again we do not need to protect the arguments, as in the R side of the interface they areobjects that are already in use. The macros

first = CADR(args);

second = CADDR(args);

third = CADDDR(args);

fourth = CAD4R(args);

provide convenient ways to access the first four arguments. More generally we can use the CDR

and CAR macros as in

args = CDR(args); a = CAR(args);

args = CDR(args); b = CAR(args);

which clearly allows us to extract an unlimited number of arguments (whereas .Call has a limit,albeit at 65 not a small one).

More usefully, the .External interface provides an easy way to handle calls with a variablenumber of arguments, as length(args) will give the number of arguments supplied (of whichthe first is ignored). We may need to know the names (‘tags’) given to the actual arguments,which we can by using the TAG macro and using something like the following example, thatprints the names and the first value of its arguments if they are vector types.

SEXP showArgs(SEXP args)

{

args = CDR(args); /* skip ’name’ */

for(int i = 0; args != R_NilValue; i++, args = CDR(args)) {

const char *name =

isNull(TAG(args)) ? "" : CHAR(PRINTNAME(TAG(args)));

SEXP el = CAR(args);

if (length(el) == 0) {

Rprintf("[%d] ’%s’ R type, length 0\n", i+1, name);

continue;

}

switch(TYPEOF(el)) {

case REALSXP:

Rprintf("[%d] ’%s’ %f\n", i+1, name, REAL(el)[0]);

break;

case LGLSXP:

case INTSXP:

Rprintf("[%d] ’%s’ %d\n", i+1, name, INTEGER(el)[0]);

break;

Chapter 5: System and foreign language interfaces 108

case CPLXSXP:

{

Rcomplex cpl = COMPLEX(el)[0];

Rprintf("[%d] ’%s’ %f + %fi\n", i+1, name, cpl.r, cpl.i);

}

break;

case STRSXP:

Rprintf("[%d] ’%s’ %s\n", i+1, name,

CHAR(STRING_ELT(el, 0)));

break;

default:

Rprintf("[%d] ’%s’ R type\n", i+1, name);

}

}

return R_NilValue;

}

This can be called by the wrapper function

showArgs <- function(...) invisible(.External("showArgs", ...))

Note that this style of programming is convenient but not necessary, as an alternative style is

showArgs1 <- function(...) invisible(.Call("showArgs1", list(...)))

The (very similar) C code is in the scripts.

5.10.3 Missing and special values

One piece of error-checking the .C call does (unless NAOK is true) is to check for missing (NA)and IEEE special values (Inf, -Inf and NaN) and give an error if any are found. With the .Callinterface these will be passed to our code. In this example the special values are no problem, asIEC60559 arithmetic will handle them correctly. In the current implementation this is also trueof NA as it is a type of NaN, but it is unwise to rely on such details. Thus we will re-write thecode to handle NAs using macros defined in R_exts/Arith.h included by R.h.

The code changes are the same in any of the versions of convolve2 or convolveE:

...

for(int i = 0; i < na; i++)

for(int j = 0; j < nb; j++)

if(ISNA(xa[i]) || ISNA(xb[j]) || ISNA(xab[i + j]))

xab[i + j] = NA_REAL;

else

xab[i + j] += xa[i] * xb[j];

...

Note that the ISNA macro, and the similar macros ISNAN (which checks for NaN or NA) andR_FINITE (which is false for NA and all the special values), only apply to numeric values of typedouble. Missingness of integers, logicals and character strings can be tested by equality to theconstants NA_INTEGER, NA_LOGICAL and NA_STRING. These and NA_REAL can be used to setelements of R vectors to NA.

The constants R_NaN, R_PosInf and R_NegInf can be used to set doubles to the specialvalues.

5.11 Evaluating R expressions from C

The main function we will use is

Chapter 5: System and foreign language interfaces 109

SEXP eval(SEXP expr, SEXP rho);

the equivalent of the interpreted R code eval(expr, envir = rho) (so rho must be an environ-ment), although we can also make use of findVar, defineVar and findFun (which restricts thesearch to functions).

To see how this might be applied, here is a simplified internal version of lapply for expres-sions, used as

a <- list(a = 1:5, b = rnorm(10), test = runif(100))

.Call("lapply", a, quote(sum(x)), new.env())

with C code

SEXP lapply(SEXP list, SEXP expr, SEXP rho)

{

int n = length(list);

SEXP ans;

if(!isNewList(list)) error("’list’ must be a list");

if(!isEnvironment(rho)) error("’rho’ should be an environment");

ans = PROTECT(allocVector(VECSXP, n));

for(int i = 0; i < n; i++) {

defineVar(install("x"), VECTOR_ELT(list, i), rho);

SET_VECTOR_ELT(ans, i, eval(expr, rho));

}

setAttrib(ans, R_NamesSymbol, getAttrib(list, R_NamesSymbol));

UNPROTECT(1);

return ans;

}

It would be closer to lapply if we could pass in a function rather than an expression. Oneway to do this is via interpreted R code as in the next example, but it is possible (if somewhatobscure) to do this in C code. The following is based on the code in src/main/optimize.c.

SEXP lapply2(SEXP list, SEXP fn, SEXP rho)

{

int n = length(list);

SEXP R_fcall, ans;

if(!isNewList(list)) error("’list’ must be a list");

if(!isFunction(fn)) error("’fn’ must be a function");

if(!isEnvironment(rho)) error("’rho’ should be an environment");

R_fcall = PROTECT(lang2(fn, R_NilValue));

ans = PROTECT(allocVector(VECSXP, n));

for(int i = 0; i < n; i++) {

SETCADR(R_fcall, VECTOR_ELT(list, i));

SET_VECTOR_ELT(ans, i, eval(R_fcall, rho));

}

setAttrib(ans, R_NamesSymbol, getAttrib(list, R_NamesSymbol));

UNPROTECT(2);

return ans;

}

used by

.Call("lapply2", a, sum, new.env())

Function lang2 creates an executable pairlist of two elements, but this will only be clear tothose with a knowledge of a LISP-like language.

Chapter 5: System and foreign language interfaces 110

As a more comprehensive example of constructing an R call in C code and evaluating, considerthe following fragment of printAttributes in src/main/print.c.

/* Need to construct a call to

print(CAR(a), digits=digits)

based on the R_print structure, then eval(call, env).

See do_docall for the template for this sort of thing.

*/

SEXP s, t;

t = s = PROTECT(allocList(3));

SET_TYPEOF(s, LANGSXP);

SETCAR(t, install("print")); t = CDR(t);

SETCAR(t, CAR(a)); t = CDR(t);

SETCAR(t, ScalarInteger(digits));

SET_TAG(t, install("digits"));

eval(s, env);

UNPROTECT(1);

At this point CAR(a) is the R object to be printed, the current attribute. There are three steps:the call is constructed as a pairlist of length 3, the list is filled in, and the expression representedby the pairlist is evaluated.

A pairlist is quite distinct from a generic vector list, the only user-visible form of list in R. Apairlist is a linked list (with CDR(t) computing the next entry), with items (accessed by CAR(t))and names or tags (set by SET_TAG). In this call there are to be three items, a symbol (pointingto the function to be called) and two argument values, the first unnamed and the second named.Setting the type to LANGSXP makes this a call which can be evaluated.

5.11.1 Zero-finding

In this section we re-work the example of Becker, Chambers & Wilks (1988, pp.~205–10) onfinding a zero of a univariate function. The R code and an example are

zero <- function(f, guesses, tol = 1e-7) {

f.check <- function(x) {

x <- f(x)

if(!is.numeric(x)) stop("Need a numeric result")

as.double(x)

}

.Call("zero", body(f.check), as.double(guesses), as.double(tol),

new.env())

}

cube1 <- function(x) (x^2 + 1) * (x - 1.5)

zero(cube1, c(0, 5))

where this time we do the coercion and error-checking in the R code. The C code is

SEXP mkans(double x)

{

SEXP ans;

ans = PROTECT(allocVector(REALSXP, 1));

REAL(ans)[0] = x;

UNPROTECT(1);

return ans;

}

Chapter 5: System and foreign language interfaces 111

double feval(double x, SEXP f, SEXP rho)

{

defineVar(install("x"), mkans(x), rho);

return REAL(eval(f, rho))[0];

}

SEXP zero(SEXP f, SEXP guesses, SEXP stol, SEXP rho)

{

double x0 = REAL(guesses)[0], x1 = REAL(guesses)[1],

tol = REAL(stol)[0];

double f0, f1, fc, xc;

if(tol <= 0.0) error("non-positive tol value");

f0 = feval(x0, f, rho); f1 = feval(x1, f, rho);

if(f0 == 0.0) return mkans(x0);

if(f1 == 0.0) return mkans(x1);

if(f0*f1 > 0.0) error("x[0] and x[1] have the same sign");

for(;;) {

xc = 0.5*(x0+x1);

if(fabs(x0-x1) < tol) return mkans(xc);

fc = feval(xc, f, rho);

if(fc == 0) return mkans(xc);

if(f0*fc > 0.0) {

x0 = xc; f0 = fc;

} else {

x1 = xc; f1 = fc;

}

}

}

5.11.2 Calculating numerical derivatives

We will use a longer example (by Saikat DebRoy) to illustrate the use of evaluation and.External. This calculates numerical derivatives, something that could be done as effectivelyin interpreted R code but may be needed as part of a larger C calculation.

An interpreted R version and an example are

Chapter 5: System and foreign language interfaces 112

numeric.deriv <- function(expr, theta, rho=sys.frame(sys.parent()))

{

eps <- sqrt(.Machine$double.eps)

ans <- eval(substitute(expr), rho)

grad <- matrix(, length(ans), length(theta),

dimnames=list(NULL, theta))

for (i in seq_along(theta)) {

old <- get(theta[i], envir=rho)

delta <- eps * max(1, abs(old))

assign(theta[i], old+delta, envir=rho)

ans1 <- eval(substitute(expr), rho)

assign(theta[i], old, envir=rho)

grad[, i] <- (ans1 - ans)/delta

}

attr(ans, "gradient") <- grad

ans

}

omega <- 1:5; x <- 1; y <- 2

numeric.deriv(sin(omega*x*y), c("x", "y"))

where expr is an expression, theta a character vector of variable names and rho the environmentto be used.

For the compiled version the call from R will be

.External("numeric_deriv", expr, theta, rho)

with example usage

.External("numeric_deriv", quote(sin(omega*x*y)),

c("x", "y"), .GlobalEnv)

Note the need to quote the expression to stop it being evaluated in the caller.

Here is the complete C code which we will explain section by section.

#include <R.h> /* for DOUBLE_EPS */

#include <Rinternals.h>

SEXP numeric_deriv(SEXP args)

{

SEXP theta, expr, rho, ans, ans1, gradient, par, dimnames;

double tt, xx, delta, eps = sqrt(DOUBLE_EPS), *rgr, *rans;

int i, start;

expr = CADR(args);

if(!isString(theta = CADDR(args)))

error("theta should be of type character");

if(!isEnvironment(rho = CADDDR(args)))

error("rho should be an environment");

ans = PROTECT(coerceVector(eval(expr, rho), REALSXP));

gradient = PROTECT(allocMatrix(REALSXP, LENGTH(ans), LENGTH(theta)));

rgr = REAL(gradient); rans = REAL(ans);

Chapter 5: System and foreign language interfaces 113

for(i = 0, start = 0; i < LENGTH(theta); i++, start += LENGTH(ans)) {

par = PROTECT(findVar(install(CHAR(STRING_ELT(theta, i))), rho));

tt = REAL(par)[0];

xx = fabs(tt);

delta = (xx < 1) ? eps : xx*eps;

REAL(par)[0] += delta;

ans1 = PROTECT(coerceVector(eval(expr, rho), REALSXP));

for(int j = 0; j < LENGTH(ans); j++)

rgr[j + start] = (REAL(ans1)[j] - rans[j])/delta;

REAL(par)[0] = tt;

UNPROTECT(2); /* par, ans1 */

}

dimnames = PROTECT(allocVector(VECSXP, 2));

SET_VECTOR_ELT(dimnames, 1, theta);

dimnamesgets(gradient, dimnames);

setAttrib(ans, install("gradient"), gradient);

UNPROTECT(3); /* ans gradient dimnames */

return ans;

}

The code to handle the arguments is

expr = CADR(args);

if(!isString(theta = CADDR(args)))

error("theta should be of type character");

if(!isEnvironment(rho = CADDDR(args)))

error("rho should be an environment");

Note that we check for correct types of theta and rho but do not check the type of expr. Thatis because eval can handle many types of R objects other than EXPRSXP. There is no usefulcoercion we can do, so we stop with an error message if the arguments are not of the correctmode.

The first step in the code is to evaluate the expression in the environment rho, by

ans = PROTECT(coerceVector(eval(expr, rho), REALSXP));

We then allocate space for the calculated derivative by

gradient = PROTECT(allocMatrix(REALSXP, LENGTH(ans), LENGTH(theta)));

The first argument to allocMatrix gives the SEXPTYPE of the matrix: here we want it to beREALSXP. The other two arguments are the numbers of rows and columns.

for(i = 0, start = 0; i < LENGTH(theta); i++, start += LENGTH(ans)) {

par = PROTECT(findVar(install(CHAR(STRING_ELT(theta, i))), rho));

Here, we are entering a for loop. We loop through each of the variables. In the for loop, wefirst create a symbol corresponding to the i’th element of the STRSXP theta. Here, STRING_ELT(theta, i) accesses the i’th element of the STRSXP theta. Macro CHAR() extracts theactual character representation11 of it: it returns a pointer. We then install the name and usefindVar to find its value.

tt = REAL(par)[0];

xx = fabs(tt);

delta = (xx < 1) ? eps : xx*eps;

REAL(par)[0] += delta;

ans1 = PROTECT(coerceVector(eval(expr, rho), REALSXP));

11 see Section 5.15 [Character encoding issues], page 118 for why this might not be what is required.

Chapter 5: System and foreign language interfaces 114

We first extract the real value of the parameter, then calculate delta, the increment to beused for approximating the numerical derivative. Then we change the value stored in par (inenvironment rho) by delta and evaluate expr in environment rho again. Because we are directlydealing with original R memory locations here, R does the evaluation for the changed parametervalue.

for(int j = 0; j < LENGTH(ans); j++)

rgr[j + start] = (REAL(ans1)[j] - rans[j])/delta;

REAL(par)[0] = tt;

UNPROTECT(2);

}

Now, we compute the i’th column of the gradient matrix. Note how it is accessed: R storesmatrices by column (like FORTRAN).

dimnames = PROTECT(allocVector(VECSXP, 2));

SET_VECTOR_ELT(dimnames, 1, theta);

dimnamesgets(gradient, dimnames);

setAttrib(ans, install("gradient"), gradient);

UNPROTECT(3);

return ans;

}

First we add column names to the gradient matrix. This is done by allocating a list (a VECSXP)whose first element, the row names, is NULL (the default) and the second element, the col-umn names, is set as theta. This list is then assigned as the attribute having the symbolR_DimNamesSymbol. Finally we set the gradient matrix as the gradient attribute of ans, unpro-tect the remaining protected locations and return the answer ans.

5.12 Parsing R code from C

Suppose an R extension want to accept an R expression from the user and evaluate it. Theprevious section covered evaluation, but the expression will be entered as text and needs to beparsed first. A small part of R’s parse interface is declared in header file R_ext/Parse.h12.

An example of the usage can be found in the (example) Windows package windlgs includedin the R source tree. The essential part is

12 This is only guaranteed to show the current interface: it is liable to change.

Chapter 5: System and foreign language interfaces 115

#include <R.h>

#include <Rinternals.h>

#include <R_ext/Parse.h>

SEXP menu_ttest3()

{

char cmd[256];

SEXP cmdSexp, cmdexpr, ans = R_NilValue;

ParseStatus status;

...

if(done == 1) {

cmdSexp = PROTECT(allocVector(STRSXP, 1));

SET_STRING_ELT(cmdSexp, 0, mkChar(cmd));

cmdexpr = PROTECT(R_ParseVector(cmdSexp, -1, &status, R_NilValue));

if (status != PARSE_OK) {

UNPROTECT(2);

error("invalid call %s", cmd);

}

/* Loop is needed here as EXPSEXP will be of length > 1 */

for(int i = 0; i < length(cmdexpr); i++)

ans = eval(VECTOR_ELT(cmdexpr, i), R_GlobalEnv);

UNPROTECT(2);

}

return ans;

}

Note that a single line of text may give rise to more than one R expression.

R_ParseVector is essentially the code used to implement parse(text=) at R level. The firstargument is a character vector (corresponding to text) and the second the maximal numberof expressions to parse (corresponding to n). The third argument is a pointer to a variable ofan enumeration type, and it is normal (as parse does) to regard all values other than PARSE_

OK as an error. Other values which might be returned are PARSE_INCOMPLETE (an incompleteexpression was found) and PARSE_ERROR (a syntax error), in both cases the value returned beingR_NilValue. The fourth argument is a length one character vector to be used as a filename inerror messages, a srcfile object or the R NULL object (as in the example above). If a srcfile

object was used, a srcref attribute would be attached to the result, containing a list of srcrefobjects of the same length as the expression, to allow it to be echoed with its original formatting.

5.12.1 Accessing source references

The source references added by the parser are recorded by R’s evaluator as it evaluates code.Two functions make these available to debuggers running C code:

SEXP R_GetCurrentSrcref(int skip);

This function checks R_Srcref and the current evaluation stack for entries that containsource reference information. The skip argument tells how many source references to skipbefore returning the SEXP of the srcref object, counting from the top of the stack. If skip <

0, abs(skip) locations are counted up from the bottom of the stack. If too few or no sourcereferences are found, NULL is returned.

SEXP R_GetSrcFilename(SEXP srcref);

This function extracts the filename from the source reference for display, returning a length1 character vector containing the filename. If no name is found, "" is returned.

Chapter 5: System and foreign language interfaces 116

5.13 External pointers and weak references

The SEXPTYPEs EXTPTRSXP and WEAKREFSXP can be encountered at R level, but are created inC code.

External pointer SEXPs are intended to handle references to C structures such as ‘handles’,and are used for this purpose in package RODBC for example. They are unusual in their copyingsemantics in that when an R object is copied, the external pointer object is not duplicated. (Forthis reason external pointers should only be used as part of an object with normal semantics,for example an attribute or an element of a list.)

An external pointer is created by

SEXP R_MakeExternalPtr(void *p, SEXP tag, SEXP prot);

where p is the pointer (and hence this cannot portably be a function pointer), and tag and prot

are references to ordinary R objects which will remain in existence (be protected from garbagecollection) for the lifetime of the external pointer object. A useful convention is to use the tag

field for some form of type identification and the prot field for protecting the memory that theexternal pointer represents, if that memory is allocated from the R heap. Both tag and prot

can be R_NilValue, and often are.

The elements of an external pointer can be accessed and set via

void *R_ExternalPtrAddr(SEXP s);

SEXP R_ExternalPtrTag(SEXP s);

SEXP R_ExternalPtrProtected(SEXP s);

void R_ClearExternalPtr(SEXP s);

void R_SetExternalPtrAddr(SEXP s, void *p);

void R_SetExternalPtrTag(SEXP s, SEXP tag);

void R_SetExternalPtrProtected(SEXP s, SEXP p);

Clearing a pointer sets its value to the C NULL pointer.

An external pointer object can have a finalizer, a piece of code to be run when the object isgarbage collected. This can be R code or C code, and the various interfaces are, respectively.

void R_RegisterFinalizerEx(SEXP s, SEXP fun, Rboolean onexit);

typedef void (*R_CFinalizer_t)(SEXP);

void R_RegisterCFinalizerEx(SEXP s, R_CFinalizer_t fun, Rboolean onexit);

The R function indicated by fun should be a function of a single argument, the object to befinalized. R does not perform a garbage collection when shutting down, and the onexit argumentof the extended forms can be used to ask that the finalizer be run during a normal shutdown ofthe R session. It is suggested that it is good practice to clear the pointer on finalization.

The only R level function for interacting with external pointers is reg.finalizer which canbe used to set a finalizer.

It is probably not a good idea to allow an external pointer to be saved and then reloaded,but if this happens the pointer will be set to the C NULL pointer.

Weak references are used to allow the programmer to maintain information on entities withoutpreventing the garbage collection of the entities once they become unreachable.

A weak reference contains a key and a value. The value is reachable is if it either reachabledirectly or via weak references with reachable keys. Once a value is determined to be unreachableduring garbage collection, the key and value are set to R_NilValue and the finalizer will be runlater in the garbage collection.

Weak reference objects are created by one of

SEXP R_MakeWeakRef(SEXP key, SEXP val, SEXP fin, Rboolean onexit);

SEXP R_MakeWeakRefC(SEXP key, SEXP val, R_CFinalizer_t fin,

Chapter 5: System and foreign language interfaces 117

Rboolean onexit);

where the R or C finalizer are specified in exactly the same way as for an external pointer object(whose finalization interface is implemented via weak references).

The parts can be accessed via

SEXP R_WeakRefKey(SEXP w);

SEXP R_WeakRefValue(SEXP w);

void R_RunWeakRefFinalizer(SEXP w);

A toy example of the use of weak references can be found at www.stat.uiowa.edu/~luke/R/references/weakfinex.html, but that is used to add finalizers to external pointers whichcan now be done more directly. At the time of writing no CRAN or Bioconductor package usesweak references.

5.13.1 An example

Package RODBC uses external pointers to maintain its channels, connections to databases.There can be several connections open at once, and the status information for each is stored ina C structure (pointed to by this_handle) in the code extract below) that is returned via anexternal pointer as part of the RODBC ‘channel’ (as the "handle_ptr" attribute). The externalpointer is created by

SEXP ans, ptr;

ans = PROTECT(allocVector(INTSXP, 1));

ptr = R_MakeExternalPtr(thisHandle, install("RODBC_channel"), R_NilValue);

PROTECT(ptr);

R_RegisterCFinalizerEx(ptr, chanFinalizer, TRUE);

...

/* return the channel no */

INTEGER(ans)[0] = nChannels;

/* and the connection string as an attribute */

setAttrib(ans, install("connection.string"), constr);

setAttrib(ans, install("handle_ptr"), ptr);

UNPROTECT(3);

return ans;

Note the symbol given to identify the usage of the external pointer, and the use of the finalizer.Since the final argument when registering the finalizer is TRUE, the finalizer will be run at thethe of the R session (unless it crashes). This is used to close and clean up the connection to thedatabase. The finalizer code is simply

static void chanFinalizer(SEXP ptr)

{

if(!R_ExternalPtrAddr(ptr)) return;

inRODBCClose(R_ExternalPtrAddr(ptr));

R_ClearExternalPtr(ptr); /* not really needed */

}

Clearing the pointer and checking for a NULL pointer avoids any possibility of attempting toclose an already-closed channel.

R’s connections provide another example of using external pointers, in that case purely tobe able to use a finalizer to close and destroy the connection if it is no longer is use.

5.14 Vector accessor functions

The vector accessors like REAL and INTEGER and VECTOR_ELT are functions when used in Rextensions. (For efficiency they are macros when used in the R source code, apart from SET_

STRING_ELT and SET_VECTOR_ELT which are always functions.)

Chapter 5: System and foreign language interfaces 118

The accessor functions check that they are being used on an appropriate type of SEXP.

If efficiency is essential, the macro versions of the accessors can be obtained by defining‘USE_RINTERNALS’ before including Rinternals.h. If you find it necessary to do so, please dotest that your code compiles without ‘USE_RINTERNALS’ defined, as this provides a stricter testthat the accessors have been used correctly.

5.15 Character encoding issues

CHARSXPs can be marked as coming from a known encoding (Latin-1 or UTF-8). This is mainlyintended for human-readable output, and most packages can just treat such CHARSXPs as awhole. However, if they need to be interpreted as characters or output at C level then it wouldnormally be correct to ensure that they are converted to the encoding of the current locale: thiscan be done by accessing the data in the CHARSXP by translateChar rather than by CHAR. Ifre-encoding is needed this allocates memory with R_alloc which thus persists to the end of the.Call/.External call unless vmaxset is used (see Section 6.1.1 [Transient storage allocation],page 119).

There is a similar function translateCharUTF8 which converts to UTF-8: this has the ad-vantage that a faithful translation is almost always possible (whereas only a few languages canbe represented in the encoding of the current locale unless that is UTF-8).

There is a public interface to the encoding marked on CHARXSXPs via

typedef enum {CE_NATIVE, CE_UTF8, CE_LATIN1, CE_SYMBOL, CE_ANY} cetype_t;

cetype_t getCharCE(SEXP);

SEXP mkCharCE(const char *, cetype_t);

Only CE_UTF8 and CE_LATIN1 are marked on CHARSXPs (and so Rf_getCharCE will only returnone of the first three), and these should only be used on non-ASCII strings. Value CE_SYMBOL isused internally to indicate Adobe Symbol encoding. Value CE_ANY is used to indicate a characterstring that will not need re-encoding – this is used for character strings known to be in ASCII,and can also be used as an input parameter where the intention is that the string is treated asa series of bytes. (See the comments under mkChar about the length of input allowed.)

Function

const char *reEnc(const char *x, cetype_t ce_in, cetype_t ce_out,

int subst);

can be used to re-encode character strings: like translateChar it returns a string allocated byR_alloc. This can translate from CE_SYMBOL to CE_UTF8, but not conversely. Argument substcontrols what to do with untranslatable characters or invalid input: this is done byte-by-bytewith 1 indicates to output hex of the form <a0>, and 2 to replace by ., with any other valuecausing the byte to produce no output.

There is also

SEXP mkCharLenCE(const char *, size_t, cetype_t);

to create marked character strings of a given length.

Chapter 6: The R API: entry points for C code 119

6 The R API: entry points for C code

There are a large number of entry points in the R executable/DLL that can be called from Ccode (and some that can be called from FORTRAN code). Only those documented here arestable enough that they will only be changed with considerable notice.

The recommended procedure to use these is to include the header file R.h in your C code by

#include <R.h>

This will include several other header files from the directory R_INCLUDE_DIR/R_ext, and thereare other header files there that can be included too, but many of the features they containshould be regarded as undocumented and unstable.

An alternative is to include the header file S.h, which may be useful when porting code fromS. This includes rather less than R.h, and has some extra compatibility definitions (for examplethe S_complex type from S).

The defines used for compatibility with S sometimes causes conflicts (notably with Windowsheaders), and the known problematic defines can be removed by defining STRICT_R_HEADERS.

Most of these header files, including all those included by R.h, can be used from C++ code.Some others need to be included within an extern "C" declaration, and for clarity this is ad-visable for all R header files.

Note: Because R re-maps many of its external names to avoid clashes with usercode, it is essential to include the appropriate header files when using these entrypoints.

This remapping can cause problems1, and can be eliminated by defining R_NO_REMAP andprepending ‘Rf_’ to all the function names used from Rinternals.h and R_ext/Error.h. Theseproblems can usually be avoided by including other headers (such as system headers and thosefor external software used by the package) before R.h.

We can classify the entry points as

API Entry points which are documented in this manual and declared in an installedheader file. These can be used in distributed packages and will only be changedafter deprecation.

public Entry points declared in an installed header file that are exported on all R platformsbut are not documented and subject to change without notice.

private Entry points that are used when building R and exported on all R platforms butare not declared in the installed header files. Do not use these in distributed code.

hidden Entry points that are where possible (Windows and some modern Unix-alike com-pilers/loaders when using R as a shared library) not exported.

6.1 Memory allocation

There are two types of memory allocation available to the C programmer, one in which Rmanages the clean-up and the other in which user has full control (and responsibility).

6.1.1 Transient storage allocation

Here R will reclaim the memory at the end of the call to .C, .Call or .External. Use

char *R_alloc(size_t n, int size)

which allocates n units of size bytes each. A typical usage (from package stats) is

1 Known problems are redefining error, length, vector and warning

Chapter 6: The R API: entry points for C code 120

x = (int *) R_alloc(nrows(merge)+2, sizeof(int));

(size_t is defined in stddef.h which the header defining R_alloc includes.)

There is a similar call, S_alloc (for compatibility with older versions of S) which zeroes thememory allocated,

char *S_alloc(long n, int size)

and

char *S_realloc(char *p, long new, long old, int size)

which changes the allocation size from old to new units, and zeroes the additional units.

For compatibility with current versions of S, header S.h (only) defines wrapper macros equiv-alent to

type* Salloc(long n, int type)

type* Srealloc(char *p, long new, long old, int type)

This memory is taken from the heap, and released at the end of the .C, .Call or .Externalcall. Users can also manage it, by noting the current position with a call to vmaxget andsubsequently clearing memory allocated by a call to vmaxset. An example might be

void *vmax = vmaxget()

// a loop involving the use of R_alloc at each iteration

vmaxset(vmax)

This is only recommended for experts.

Note that this memory will be freed on error or user interrupt (if allowed: see Section 6.12[Allowing interrupts], page 131).

Note that although n is size_t, there may be limits imposed by R’s internal allocationmechanism. These will only come into play on 64-bit systems, where the limit for n prior to R3.0.0 was just under 16Gb.

These functions should only be used in code called by .C etc, never from front-ends. Theyare not thread-safe.

6.1.2 User-controlled memory

The other form of memory allocation is an interface to malloc, the interface providing R errorhandling. This memory lasts until freed by the user and is additional to the memory allocatedfor the R workspace.

The interface functions are

type* Calloc(size_t n, type)

type* Realloc(any *p, size_t n, type)

void Free(any *p)

providing analogues of calloc, realloc and free. If there is an error during allocation it ishandled by R, so if these routines return the memory has been successfully allocated or freed.Free will set the pointer p to NULL. (Some but not all versions of S do so.)

Users should arrange to Free this memory when no longer needed, including on error or userinterrupt. This can often be done most conveniently from an on.exit action in the calling Rfunction – see pwilcox for an example.

Do not assume that memory allocated by Calloc/Realloc comes from the same pool as usedby malloc: in particular do not use free or strdup with it.

These entry points need to be prefixed by R_ if STRICT_R_HEADERS has been defined.

Chapter 6: The R API: entry points for C code 121

6.2 Error handling

The basic error handling routines are the equivalents of stop and warning in R code, and usethe same interface.

void error(const char * format, ...);

void warning(const char * format, ...);

These have the same call sequences as calls to printf, but in the simplest case can be calledwith a single character string argument giving the error message. (Don’t do this if the stringcontains ‘%’ or might otherwise be interpreted as a format.)

If STRICT_R_HEADERS is not defined there is also an S-compatibility interface which uses callsof the form

PROBLEM ...... ERROR

MESSAGE ...... WARN

PROBLEM ...... RECOVER(NULL_ENTRY)

MESSAGE ...... WARNING(NULL_ENTRY)

the last two being the forms available in all S versions. Here ‘......’ is a set of arguments toprintf, so can be a string or a format string followed by arguments separated by commas.

6.2.1 Error handling from FORTRAN

There are two interface function provided to call error and warning from FORTRAN code, ineach case with a simple character string argument. They are defined as

subroutine rexit(message)

subroutine rwarn(message)

Messages of more than 255 characters are truncated, with a warning.

6.3 Random number generation

The interface to R’s internal random number generation routines is

double unif_rand();

double norm_rand();

double exp_rand();

giving one uniform, normal or exponential pseudo-random variate. However, before these areused, the user must call

GetRNGstate();

and after all the required variates have been generated, call

PutRNGstate();

These essentially read in (or create) .Random.seed and write it out after use.

File S.h defines seed_in and seed_out for S-compatibility rather than GetRNGstate andPutRNGstate. These take a long * argument which is ignored.

The random number generator is private to R; there is no way to select the kind of RNG orset the seed except by evaluating calls to the R functions.

The C code behind R’s rxxx functions can be accessed by including the header file Rmath.h;See Section 6.7.1 [Distribution functions], page 124. Those calls generate a single variate andshould also be enclosed in calls to GetRNGstate and PutRNGstate.

Chapter 6: The R API: entry points for C code 122

6.4 Missing and IEEE special values

A set of functions is provided to test for NA, Inf, -Inf and NaN. These functions are accessedvia macros:

ISNA(x) True for R’s NA onlyISNAN(x) True for R’s NA and IEEE NaN

R_FINITE(x) False for Inf, -Inf, NA, NaN

and via function R_IsNaN which is true for NaN but not NA.

Do use R_FINITE rather than isfinite or finite; the latter is often mendacious andisfinite is only available on a some platforms, on which R_FINITE is a macro expandingto isfinite.

Currently in C code ISNAN is a macro calling isnan. (Since this gives problems on some C++systems, if the R headers is called from C++ code a function call is used.)

You can check for Inf or -Inf by testing equality to R_PosInf or R_NegInf, and set (butnot test) an NA as NA_REAL.

All of the above apply to double variables only. For integer variables there is a variableaccessed by the macro NA_INTEGER which can used to set or test for missingness.

6.5 Printing

The most useful function for printing from a C routine compiled into R is Rprintf. This is usedin exactly the same way as printf, but is guaranteed to write to R’s output (which might bea GUI console rather than a file, and can be re-directed by sink). It is wise to write completelines (including the "\n") before returning to R. It is defined in R_ext/Print.h.

The function REprintf is similar but writes on the error stream (stderr) which may or maynot be different from the standard output stream.

Functions Rvprintf and REvprintf are analogues using the vprintf interface. Becausethat is a C99 interface, they are only defined by R_ext/Print.h in C++ code if the macroR_USE_C99_IN_CXX is defined when it is included.

Another circumstance when it may be important to use these functions is when using parallelcomputation on a cluster of computational nodes, as their output will be re-directed/loggedappropriately.

6.5.1 Printing from FORTRAN

On many systems FORTRAN write and print statements can be used, but the output maynot interleave well with that of C, and will be invisible on GUI interfaces. They are not portableand best avoided.

Three subroutines are provided to ease the output of information from FORTRAN code.

subroutine dblepr(label, nchar, data, ndata)

subroutine realpr(label, nchar, data, ndata)

subroutine intpr (label, nchar, data, ndata)

Here label is a character label of up to 255 characters, nchar is its length (which can be -1 if thewhole label is to be used), and data is an array of length at least ndata of the appropriate type(double precision, real and integer respectively). These routines print the label on one lineand then print data as if it were an R vector on subsequent line(s). They work with zero ndata,and so can be used to print a label alone.

Chapter 6: The R API: entry points for C code 123

6.6 Calling C from FORTRAN and vice versa

Naming conventions for symbols generated by FORTRAN differ by platform: it is not safe toassume that FORTRAN names appear to C with a trailing underscore. To help cover up theplatform-specific differences there is a set of macros that should be used.

F77_SUB(name)

to define a function in C to be called from FORTRAN

F77_NAME(name)

to declare a FORTRAN routine in C before use

F77_CALL(name)

to call a FORTRAN routine from C

F77_COMDECL(name)

to declare a FORTRAN common block in C

F77_COM(name)

to access a FORTRAN common block from C

On most current platforms these are all the same, but it is unwise to rely on this. Note thatnames with underscores are not legal in FORTRAN 77, and are not portably handled by theabove macros. (Also, all FORTRAN names for use by R are lower case, but this is not enforcedby the macros.)

For example, suppose we want to call R’s normal random numbers from FORTRAN. Weneed a C wrapper along the lines of

#include <R.h>

void F77_SUB(rndstart)(void) { GetRNGstate(); }

void F77_SUB(rndend)(void) { PutRNGstate(); }

double F77_SUB(normrnd)(void) { return norm_rand(); }

to be called from FORTRAN as in

subroutine testit()

double precision normrnd, x

call rndstart()

x = normrnd()

call dblepr("X was", 5, x, 1)

call rndend()

end

Note that this is not guaranteed to be portable, for the return conventions might not be com-patible between the C and FORTRAN compilers used. (Passing values via arguments is safer.)

The standard packages, for example stats, are a rich source of further examples.

Passing character strings from C to FORTRAN 77 or vice versa is not portable (and toFortran 90 or later is even less so). We have found that it helps to ensure that a C string tobe passed is followed by several nuls (and not just the one needed as a C terminator). But formaximal portability character strings in FORTRAN should be avoided.

6.7 Numerical analysis subroutines

R contains a large number of mathematical functions for its own use, for example numericallinear algebra computations and special functions.

The header files R_ext/BLAS.h, R_ext/Lapack.h and R_ext/Linpack.h contains declara-tions of the BLAS, LAPACK and LINPACK linear algebra functions included in R. These are

Chapter 6: The R API: entry points for C code 124

expressed as calls to FORTRAN subroutines, and they will also be usable from users’ FOR-TRAN code. Although not part of the official API, this set of subroutines is unlikely to change(but might be supplemented).

The header file Rmath.h lists many other functions that are available and documented in thefollowing subsections. Many of these are C interfaces to the code behind R functions, so the Rfunction documentation may give further details.

6.7.1 Distribution functions

The routines used to calculate densities, cumulative distribution functions and quantile functionsfor the standard statistical distributions are available as entry points.

The arguments for the entry points follow the pattern of those for the normal distribution:

double dnorm(double x, double mu, double sigma, int give_log);

double pnorm(double x, double mu, double sigma, int lower_tail,

int give_log);

double qnorm(double p, double mu, double sigma, int lower_tail,

int log_p);

double rnorm(double mu, double sigma);

That is, the first argument gives the position for the density and CDF and probability for thequantile function, followed by the distribution’s parameters. Argument lower tail should beTRUE (or 1) for normal use, but can be FALSE (or 0) if the probability of the upper tail is desiredor specified.

Finally, give log should be non-zero if the result is required on log scale, and log p shouldbe non-zero if p has been specified on log scale.

Note that you directly get the cumulative (or “integrated”) hazard function, H(t) = − log(1−F (t)), by using

- pdist(t, ..., /*lower_tail = */ FALSE, /* give_log = */ TRUE)

or shorter (and more cryptic) - pdist(t, ..., 0, 1).

The random-variate generation routine rnorm returns one normal variate. See Section 6.3[Random numbers], page 121, for the protocol in using the random-variate routines.

Note that these argument sequences are (apart from the names and that rnorm has no n)mainly the same as the corresponding R functions of the same name, so the documentation of theR functions can be used. Note that the exponential and gamma distributions are parametrizedby scale rather than rate.

For reference, the following table gives the basic name (to be prefixed by ‘d’, ‘p’, ‘q’ or ‘r’apart from the exceptions noted) and distribution-specific arguments for the complete set ofdistributions.

beta beta a, bnon-central beta nbeta a, b, ncpbinomial binom n, pCauchy cauchy location, scalechi-squared chisq df

non-central chi-squared nchisq df, ncpexponential exp scale (and not rate)F f n1, n2non-central F nf n1, n2, ncpgamma gamma shape, scalegeometric geom p

hypergeometric hyper NR, NB, nlogistic logis location, scale

Chapter 6: The R API: entry points for C code 125

lognormal lnorm logmean, logsdnegative binomial nbinom size, probnormal norm mu, sigmaPoisson pois lambda

Student’s t t n

non-central t nt df, deltaStudentized range tukey (*) rr, cc, dfuniform unif a, bWeibull weibull shape, scaleWilcoxon rank sum wilcox m, nWilcoxon signed rank signrank n

Entries marked with an asterisk only have ‘p’ and ‘q’ functions available, and none of thenon-central distributions have ‘r’ functions. After a call to dwilcox, pwilcox or qwilcox thefunction wilcox_free() should be called, and similarly for the signed rank functions.

6.7.2 Mathematical functions

[Function]double gammafn (double x)[Function]double lgammafn (double x)[Function]double digamma (double x)[Function]double trigamma (double x)[Function]double tetragamma (double x)[Function]double pentagamma (double x)[Function]double psigamma (double x, double deriv)

The Gamma function, the natural logarithm of its absolute value and first four derivativesand the n-th derivative of Psi, the digamma function, which is the derivative of lgammafn. Inother words, digamma(x) is the same as (psigamma(x,0), trigamma(x) == psigamma(x,1),etc.

[Function]double beta (double a, double b)[Function]double lbeta (double a, double b)

The (complete) Beta function and its natural logarithm.

[Function]double choose (double n, double k)[Function]double lchoose (double n, double k)

The number of combinations of k items chosen from from n and the natural logarithm of itsabsolute value, generalized to arbitrary real n. k is rounded to the nearest integer (with awarning if needed).

[Function]double bessel_i (double x, double nu, double expo)[Function]double bessel_j (double x, double nu)[Function]double bessel_k (double x, double nu, double expo)[Function]double bessel_y (double x, double nu)

Bessel functions of types I, J, K and Y with index nu. For bessel_i and bessel_k thereis the option to return exp(-x) I(x; nu) or exp(x) K(x; nu) if expo is 2. (Use expo == 1 forunscaled values.)

6.7.3 Numerical Utilities

There are a few other numerical utility functions available as entry points.

Chapter 6: The R API: entry points for C code 126

[Function]double R_pow (double x, double y)[Function]double R_pow_di (double x, int i)

R_pow(x, y) and R_pow_di(x, i) compute x^y and x^i, respectively using R_FINITE checksand returning the proper result (the same as R) for the cases where x, y or i are 0 or missingor infinite or NaN.

[Function]double log1p (double x)Computes log(1 + x) (log 1 plus x ), accurately even for small x, i.e., |x| � 1.

This should be provided by your platform, in which case it is not included in Rmath.h, butis (probably) in math.h which Rmath.h includes.

[Function]double log1pmx (double x)Computes log(1 + x) - x (log 1 plus x minus x), accurately even for small x, i.e., |x| � 1.

[Function]double log1pexp (double x)Computes log(1 + exp(x)) (log 1 plus exp), accurately, notably for large x, e.g., x > 720.

[Function]double expm1 (double x)Computes exp(x) - 1 (exp x minus 1 ), accurately even for small x, i.e., |x| � 1.

This should be provided by your platform, in which case it is not included in Rmath.h, butis (probably) in math.h which Rmath.h includes.

[Function]double lgamma1p (double x)Computes log(gamma(x + 1)) (log(gamma(1 plus x))), accurately even for small x, i.e., 0 <x < 0.5.

[Function]double logspace_add (double logx, double logy)[Function]double logspace_sub (double logx, double logy)

Compute the log of a sum or difference from logs of terms, i.e., “x + y” as log (exp(logx)

+ exp(logy)) and “x - y” as log (exp(logx) - exp(logy)), without causing unnecessaryoverflows or throwing away too much accuracy.

[Function]int imax2 (int x, int y)[Function]int imin2 (int x, int y)[Function]double fmax2 (double x, double y)[Function]double fmin2 (double x, double y)

Return the larger (max) or smaller (min) of two integer or double numbers, respectively. Notethat fmax2 and fmin2 differ from C99’s fmax and fmin when one of the arguments is a NaN:these versions return NaN.

[Function]double sign (double x)Compute the signum function, where sign(x) is 1, 0, or −1, when x is positive, 0, or negative,respectively, and NaN if x is a NaN.

[Function]double fsign (double x, double y)Performs “transfer of sign” and is defined as |x| ∗ sign(y).

[Function]double fprec (double x, double digits)Returns the value of x rounded to digits decimal digits (after the decimal point).

This is the function used by R’s signif().

[Function]double fround (double x, double digits)Returns the value of x rounded to digits significant decimal digits.

This is the function used by R’s round().

[Function]double ftrunc (double x)Returns the value of x truncated (to an integer value) towards zero.

Chapter 6: The R API: entry points for C code 127

6.7.4 Mathematical constants

R has a set of commonly used mathematical constants encompassing constants usually foundmath.h and contains further ones that are used in statistical computations. All these are definedto (at least) 30 digits accuracy in Rmath.h. The following definitions use ln(x) for the naturallogarithm (log(x) in R).

Name Definition (ln = log) round(value, 7)M_E e 2.7182818M_LOG2E log2(e) 1.4426950M_LOG10E log10(e) 0.4342945M_LN2 ln(2) 0.6931472M_LN10 ln(10) 2.3025851M_PI π 3.1415927M_PI_2 π/2 1.5707963M_PI_4 π/4 0.7853982M_1_PI 1/π 0.3183099M_2_PI 2/π 0.6366198M_2_SQRTPI 2/sqrt(π) 1.1283792M_SQRT2 sqrt(2) 1.4142136M_SQRT1_2 1/sqrt(2) 0.7071068M_SQRT_3 sqrt(3) 1.7320508M_SQRT_32 sqrt(32) 5.6568542M_LOG10_2 log10(2) 0.3010300M_2PI 2π 6.2831853M_SQRT_PI sqrt(π) 1.7724539M_1_SQRT_2PI 1/sqrt(2π) 0.3989423M_SQRT_2dPI sqrt(2/π) 0.7978846M_LN_SQRT_PI ln(sqrt(π)) 0.5723649M_LN_SQRT_2PI ln(sqrt(2π)) 0.9189385M_LN_SQRT_PId2 ln(sqrt(π/2)) 0.2257914

There are a set of constants (PI, DOUBLE_EPS) (and so on) defined (unless STRICT_R_HEADERSis defined) in the included header R_ext/Constants.h, mainly for compatibility with S.

Further, the included header R_ext/Boolean.h has enumeration constants TRUE and FALSE

of type Rboolean in order to provide a way of using “logical” variables in C consistently. Thiscan conflict with other software: for example it conflicts with the headers in IJG’s jpeg-9 (butnot earlier versions).

6.8 Optimization

The C code underlying optim can be accessed directly. The user needs to supply a function tocompute the function to be minimized, of the type

typedef double optimfn(int n, double *par, void *ex);

where the first argument is the number of parameters in the second argument. The thirdargument is a pointer passed down from the calling routine, normally used to carry auxiliaryinformation.

Some of the methods also require a gradient function

typedef void optimgr(int n, double *par, double *gr, void *ex);

which passes back the gradient in the gr argument. No function is provided for finite-differencing,nor for approximating the Hessian at the result.

The interfaces (defined in header R_ext/Applic.h) are

Chapter 6: The R API: entry points for C code 128

• Nelder Mead:

void nmmin(int n, double *xin, double *x, double *Fmin, optimfn fn,

int *fail, double abstol, double intol, void *ex,

double alpha, double beta, double gamma, int trace,

int *fncount, int maxit);

• BFGS:

void vmmin(int n, double *x, double *Fmin,

optimfn fn, optimgr gr, int maxit, int trace,

int *mask, double abstol, double reltol, int nREPORT,

void *ex, int *fncount, int *grcount, int *fail);

• Conjugate gradients:

void cgmin(int n, double *xin, double *x, double *Fmin,

optimfn fn, optimgr gr, int *fail, double abstol,

double intol, void *ex, int type, int trace,

int *fncount, int *grcount, int maxit);

• Limited-memory BFGS with bounds:

void lbfgsb(int n, int lmm, double *x, double *lower,

double *upper, int *nbd, double *Fmin, optimfn fn,

optimgr gr, int *fail, void *ex, double factr,

double pgtol, int *fncount, int *grcount,

int maxit, char *msg, int trace, int nREPORT);

• Simulated annealing:

void samin(int n, double *x, double *Fmin, optimfn fn, int maxit,

int tmax, double temp, int trace, void *ex);

Many of the arguments are common to the various methods. n is the number of parameters, xor xin is the starting parameters on entry and x the final parameters on exit, with final valuereturned in Fmin. Most of the other parameters can be found from the help page for optim: seethe source code src/appl/lbfgsb.c for the values of nbd, which specifies which bounds are tobe used.

6.9 Integration

The C code underlying integrate can be accessed directly. The user needs to supply a vector-izing C function to compute the function to be integrated, of the type

typedef void integr_fn(double *x, int n, void *ex);

where x[] is both input and output and has length n, i.e., a C function, say fn, of type integr_fn must basically do for(i in 1:n) x[i] := f(x[i], ex). The vectorization requirement canbe used to speed up the integrand instead of calling it n times. Note that in the currentimplementation built on QUADPACK, n will be either 15 or 21. The ex argument is a pointerpassed down from the calling routine, normally used to carry auxiliary information.

There are interfaces (defined in header R_ext/Applic.h) for definite and for indefinite inte-grals. ‘Indefinite’ means that at least one of the integration boundaries is not finite.

• Finite:

void Rdqags(integr_fn f, void *ex, double *a, double *b,

double *epsabs, double *epsrel,

double *result, double *abserr, int *neval, int *ier,

int *limit, int *lenw, int *last,

int *iwork, double *work);

• Indefinite:

Chapter 6: The R API: entry points for C code 129

void Rdqagi(integr_fn f, void *ex, double *bound, int *inf,

double *epsabs, double *epsrel,

double *result, double *abserr, int *neval, int *ier,

int *limit, int *lenw, int *last,

int *iwork, double *work);

Only the 3rd and 4th argument differ for the two integrators; for the definite integral, usingRdqags, a and b are the integration interval bounds, whereas for an indefinite integral, usingRdqagi, bound is the finite bound of the integration (if the integral is not doubly-infinite) andinf is a code indicating the kind of integration range,

inf = 1 corresponds to (bound, +Inf),

inf = -1 corresponds to (-Inf, bound),

inf = 2 corresponds to (-Inf, +Inf),

f and ex define the integrand function, see above; epsabs and epsrel specify the absoluteand relative accuracy requested, result, abserr and last are the output components value,abs.err and subdivisions of the R function integrate, where neval gives the number ofintegrand function evaluations, and the error code ier is translated to R’s integrate() $

message, look at that function definition. limit corresponds to integrate(..., subdivisions

= *). It seems you should always define the two work arrays and the length of the second oneas

lenw = 4 * limit;

iwork = (int *) R_alloc(limit, sizeof(int));

work = (double *) R_alloc(lenw, sizeof(double));

The comments in the source code in src/appl/integrate.c give more details, particularlyabout reasons for failure (ier >= 1).

6.10 Utility functions

R has a fairly comprehensive set of sort routines which are made available to users’ C code. Thefollowing is declared in header file Rinternals.h.

[Function]void R_orderVector (int* indx, int n, SEXP arglist, Rboolean nalast,Rboolean decreasing)

This corresponds to R’s order(..., na.last, decreasing). More specifically, indx

<- order(x, y, na.last, decreasing) corresponds to R_orderVector(indx, n,

Rf_lang2(x, y), nalast, decreasing) and for three vectors, Rf_lang3(x,y,z) is used asarglist.

Note that R_orderVector() assumes the vector indx to be allocated to length ≥ n. Onreturn, indx[] contains a permutation of 0:(n-1), i.e., 0-based C indices (and not 1-basedR indices, as R’s order()).

All other sort routines are declared in header file R_ext/Utils.h (included by R.h) andinclude the following.

[Function]void R_isort (int* x, int n)[Function]void R_rsort (double* x, int n)[Function]void R_csort (Rcomplex* x, int n)[Function]void rsort_with_index (double* x, int* index, int n)

The first three sort integer, real (double) and complex data respectively. (Complex numbersare sorted by the real part first then the imaginary part.) NAs are sorted last.

rsort_with_index sorts on x, and applies the same permutation to index. NAs are sortedlast.

Chapter 6: The R API: entry points for C code 130

[Function]void revsort (double* x, int* index, int n)Is similar to rsort_with_index but sorts into decreasing order, and NAs are not handled.

[Function]void iPsort (int* x, int n, int k)[Function]void rPsort (double* x, int n, int k)[Function]void cPsort (Rcomplex* x, int n, int k)

These all provide (very) partial sorting: they permute x so that x[k] is in the correct placewith smaller values to the left, larger ones to the right.

[Function]void R_qsort (double *v, size t i, size t j)[Function]void R_qsort_I (double *v, int *I, int i, int j)[Function]void R_qsort_int (int *iv, size t i, size t j)[Function]void R_qsort_int_I (int *iv, int *I, int i, int j)

These routines sort v[i:j] or iv[i:j] (using 1-indexing, i.e., v[1] is the first element)calling the quicksort algorithm as used by R’s sort(v, method = "quick") and documentedon the help page for the R function sort. The ..._I() versions also return the sort.index()vector in I. Note that the ordering is not stable, so tied values may be permuted.

Note that NAs are not handled (explicitly) and you should use different sorting functions ifNAs can be present.

[Function]subroutine qsort4 (double precision v, integer indx, integer ii, integer jj)[Function]subroutine qsort3 (double precision v, integer ii, integer jj)

The FORTRAN interface routines for sorting double precision vectors are qsort3 and qsort4,equivalent to R_qsort and R_qsort_I, respectively.

[Function]void R_max_col (double* matrix, int* nr, int* nc, int* maxes, int*ties_meth)

Given the nr by nc matrix matrix in column-major (“FORTRAN”) order, R_max_col()returns in maxes[i-1] the column number of the maximal element in the i-th row (the sameas R’s max.col() function). In the case of ties (multiple maxima), *ties_meth is an integercode in 1:3 determining the method: 1 = “random”, 2 = “first” and 3 = “last”. See R’shelp page ?max.col.

[Function]int findInterval (double* xt, int n, double x, Rbooleanrightmost_closed, Rboolean all_inside, int ilo, int* mflag)

Given the ordered vector xt of length n, return the interval or index of x in xt[], typicallymax(i; 1 ≤ i ≤ n & xt[i] ≤ x) where we use 1-indexing as in R and FORTRAN (but not C).If rightmost closed is true, also returns n−1 if x equals xt[n]. If all inside is not 0, the resultis coerced to lie in 1:(n-1) even when x is outside the xt[] range. On return, *mflag equals−1 if x < xt[1], +1 if x >= xt[n], and 0 otherwise.

The algorithm is particularly fast when ilo is set to the last result of findInterval() and xis a value of a sequence which is increasing or decreasing for subsequent calls.

There is also an F77_CALL(interv)() version of findInterval() with the same arguments,but all pointers.

A system-independent interface to produce the name of a temporary file is provided as

[Function]char * R_tmpnam (const char *prefix, const char *tmpdir)[Function]char * R_tmpnam2 (const char *prefix, const char *tmpdir, const char

*fileext)Return a pathname for a temporary file with name beginning with prefix and ending withfileext in directory tmpdir. A NULL prefix or extension is replaced by "". Note that thereturn value is malloced and should be freed when no longer needed (unlike the system calltmpnam).

Chapter 6: The R API: entry points for C code 131

There is also the internal function used to expand file names in several R functions, andcalled directly by path.expand.

[Function]const char * R_ExpandFileName (const char *fn)Expand a path name fn by replacing a leading tilde by the user’s home directory (if defined).The precise meaning is platform-specific; it will usually be taken from the environment vari-able HOME if this is defined.

For historical reasons there are FORTRAN interfaces to functions D1MACH and I1MACH. Thesecan be called from C code as e.g. F77_CALL(d1mach)(4). Note that these are emulations of theoriginal functions by Fox, Hall and Schryer on NetLib at http://www.netlib.org/slatec/

src/ for IEC 60559 arithmetic (required by R).

6.11 Re-encoding

R has its own C-level interface to the encoding conversion capabilities provided by iconv becausethere are incompatibilities between the declarations in different implementations of iconv.

These are declared in header file R_ext/Riconv.h.

[Function]void * Riconv_open (const char *to, const char *from)Set up a pointer to an encoding object to be used to convert between two encodings: ""

indicates the current locale.

[Function]size_t Riconv (void *cd, const char **inbuf, size t *inbytesleft, char**outbuf, size t *outbytesleft)

Convert as much as possible of inbuf to outbuf. Initially the int variables indicate thenumber of bytes available in the buffers, and they are updated (and the char pointers areupdated to point to the next free byte in the buffer). The return value is the number ofcharacters converted, or (size_t)-1 (beware: size_t is usually an unsigned type). It shouldbe safe to assume that an error condition sets errno to one of E2BIG (the output buffer isfull), EILSEQ (the input cannot be converted, and might be invalid in the encoding specified) orEINVAL (the input does not end with a complete multi-byte character).

[Function]int Riconv_close (void * cd)Free the resources of an encoding object.

6.12 Allowing interrupts

No port of R can be interrupted whilst running long computations in compiled code, so pro-grammers should make provision for the code to be interrupted at suitable points by callingfrom C

#include <R_ext/Utils.h>

void R_CheckUserInterrupt(void);

and from FORTRAN

subroutine rchkusr()

These check if the user has requested an interrupt, and if so branch to R’s error handlingfunctions.

Note that it is possible that the code behind one of the entry points defined here if calledfrom your C or FORTRAN code could be interruptible or generate an error and so not returnto your code.

Chapter 6: The R API: entry points for C code 132

6.13 Platform and version information

The header files define USING_R, which can be used to test if the code is indeed being used withR.

Header file Rconfig.h (included by R.h) is used to define platform-specific macros that aremainly for use in other header files. The macro WORDS_BIGENDIAN is defined on big-endian2

systems (e.g. most OSes on Sparc and PowerPC hardware) and not on little-endian systems(such as i686 and x86_64 on all OSes, and Linux on Alpha and Itanium). It can be useful whenmanipulating binary files. The macro SUPPORT_OPENMP is defined on suitable systems and canbe used in conjunction with the SUPPORT_OPENMP_* macros in packages that want to make useof OpenMP.

Header file Rversion.h (not included by R.h) defines a macro R_VERSION giving the versionnumber encoded as an integer, plus a macro R_Version to do the encoding. This can be used totest if the version of R is late enough, or to include back-compatibility features. For protectionagainst very old versions of R which did not have this macro, use a construction such as

#if defined(R_VERSION) && R_VERSION >= R_Version(1, 9, 0)

...

#endif

More detailed information is available in the macros R_MAJOR, R_MINOR, R_YEAR, R_MONTH andR_DAY: see the header file Rversion.h for their format. Note that the minor version includesthe patchlevel (as in ‘9.0’).

6.14 Inlining C functions

The C99 keyword inline should be recognized by all compilers now used to build R. Portablecode which might be used with earlier versions of R can be written using the macro R_INLINE

(defined in file Rconfig.h included by R.h), as for example from package cluster

#include <R.h>

static R_INLINE int ind_2(int l, int j)

{

...

}

Be aware that using inlining with functions in more than one compilation unit is almostimpossible to do portably, see http://www.greenend.org.uk/rjk/2003/03/inline.html, sothis usage is for static functions as in the example. All the R configure code has checked isthat R_INLINE can be used in a single C file with the compiler used to build R. We recommendthat packages making extensive use of inlining include their own configure code.

6.15 Controlling visibility

Header R_ext/Visibility has some definitions for controlling the visibility of entry points.These are only effective when ‘HAVE_VISIBILITY_ATTRIBUTE’ is defined – this is checked whenR is configured and recorded in header Rconfig.h (included by R_ext/Visibility.h). It isgenerally defined on modern Unix-alikes with a recent compiler, but not supported on OS X norWindows. Minimizing the visibility of symbols in a shared library will both speed up its loading(unlikely to be significant) and reduce the possibility of linking to the wrong entry points of thesame name.

C/C++ entry points prefixed by attribute_hidden will not be visible in the shared object.There is no comparable mechanism for FORTRAN entry points, but there is a more comprehen-sive scheme used by, for example package stats. Most compilers which allow control of visibility

2 http://en.wikipedia.org/wiki/Endianness.

Chapter 6: The R API: entry points for C code 133

will allow control of visibility for all symbols via a flag, and where known the flag is encapsulatedin the macros ‘C_VISIBILITY’ and F77_VISIBILITY for C and FORTRAN compilers. These aredefined in etc/Makeconf and so available for normal compilation of package code. For example,src/Makevars could include

PKG_CFLAGS=$(C_VISIBILITY)

PKG_FFLAGS=$(F77_VISIBILITY)

This would end up with no visible entry points, which would be pointless. However, theeffect of the flags can be overridden by using the attribute_visible prefix. A shared objectwhich registers its entry points needs only for have one visible entry point, its initializer, so forexample package stats has

void attribute_visible R_init_stats(DllInfo *dll)

{

R_registerRoutines(dll, CEntries, CallEntries, FortEntries, NULL);

R_useDynamicSymbols(dll, FALSE);

...

}

The visibility mechanism is not available on Windows, but there is an equallyeffective way to control which entry points are visible, by supplying a definitions filepkgnme/src/pkgname-win.def: only entry points listed in that file will be visible. Again usingstats as an example, it has

LIBRARY stats.dll

EXPORTS

R_init_stats

6.16 Using these functions in your own C code

It is possible to build Mathlib, the R set of mathematical functions documented in Rmath.h, as astandalone library libRmath under both Unix-alikes and Windows. (This includes the functionsdocumented in Section 6.7 [Numerical analysis subroutines], page 123 as from that header file.)

The library is not built automatically when R is installed, but can be built in the directorysrc/nmath/standalone in the R sources: see the file README there. To use the code in yourown C program include

#define MATHLIB_STANDALONE

#include <Rmath.h>

and link against ‘-lRmath’ (and perhaps ‘-lm’). There is an example file test.c.

A little care is needed to use the random-number routines. You will need to supply theuniform random number generator

double unif_rand(void)

or use the one supplied (and with a dynamic library or DLL you will have to use the one supplied,which is the Marsaglia-multicarry with an entry points

set_seed(unsigned int, unsigned int)

to set its seeds and

get_seed(unsigned int *, unsigned int *)

to read the seeds).

Chapter 6: The R API: entry points for C code 134

6.17 Organization of header files

The header files which R installs are in directory R_INCLUDE_DIR (default R_HOME/include).This currently includes

R.h includes many other filesS.h different version for code ported from SRinternals.h definitions for using R’s internal structuresRdefines.h macros for an S-like interface to the above (no

longer maintained)

Rmath.h standalone math libraryRversion.h R version informationRinterface.h for add-on front-ends (Unix-alikes only)Rembedded.h for add-on front-endsR_ext/Applic.h optimization and integrationR_ext/BLAS.h C definitions for BLAS routinesR_ext/Callbacks.h C (and R function) top-level task handlersR_ext/GetX11Image.h X11Image interface used by package trkplotR_ext/Lapack.h C definitions for some LAPACK routinesR_ext/Linpack.h C definitions for some LINPACK routines, not all

of which are included in R

R_ext/Parse.h a small part of R’s parse interface: not part of thestable API.

R_ext/RStartup.h for add-on front-endsR_ext/Rdynload.h needed to register compiled code in packagesR_ext/R-ftp-http.h interface to internal method of download.fileR_ext/Riconv.h interface to iconv

R_ext/Visibility.h definitions controlling visibilityR_ext/eventloop.h for add-on front-ends and for packages that need

to share in the R event loops (on all platforms)

The following headers are included by R.h:

Rconfig.h configuration info that is made availableR_ext/Arith.h handling for NAs, NaNs, Inf/-InfR_ext/Boolean.h TRUE/FALSE typeR_ext/Complex.h C typedefs for R’s complexR_ext/Constants.h constantsR_ext/Error.h error handlingR_ext/Memory.h memory allocationR_ext/Print.h Rprintf and variations.R_ext/RS.h definitions common to R.h and S.h, including

F77_CALL etc.

R_ext/Random.h random number generationR_ext/Utils.h sorting and other utilitiesR_ext/libextern.h definitions for exports from R.dll on Windows.

The graphics systems are exposed in headers R_ext/GraphicsEngine.h, R_

ext/GraphicsDevice.h (which it includes) and R_ext/QuartzDevice.h. Facilities for definingcustom connection implementations are provided in R_ext/Connections.h, but make sure youconsult the file before use.

Chapter 7: Generic functions and methods 135

7 Generic functions and methods

R programmers will often want to add methods for existing generic functions, and may want toadd new generic functions or make existing functions generic. In this chapter we give guidelinesfor doing so, with examples of the problems caused by not adhering to them.

This chapter only covers the ‘informal’ class system copied from S3, and not with the S4(formal) methods of package methods.

The key function for methods is NextMethod, which dispatches the next method. It is quitetypical for a method function to make a few changes to its arguments, dispatch to the nextmethod, receive the results and modify them a little. An example is

t.data.frame <- function(x)

{

x <- as.matrix(x)

NextMethod("t")

}

Also consider predict.glm: it happens that in R for historical reasons it calls predict.lm di-rectly, but in principle (and in S originally and currently) it could use NextMethod. (NextMethodseems under-used in the R sources. Do be aware that there are S/R differences in this area, andthe example above works because there is a next method, the default method, not that a newmethod is selected when the class is changed.)

Any method a programmer writes may be invoked from another method by NextMethod,with the arguments appropriate to the previous method. Further, the programmer cannot predictwhich method NextMethod will pick (it might be one not yet dreamt of), and the end user callingthe generic needs to be able to pass arguments to the next method. For this to work

A method must have all the arguments of the generic, including ... if the genericdoes.

It is a grave misunderstanding to think that a method needs only to accept the arguments itneeds. The original S version of predict.lm did not have a ... argument, although predict

did. It soon became clear that predict.glm needed an argument dispersion to handle over-dispersion. As predict.lm had neither a dispersion nor a ... argument, NextMethod couldno longer be used. (The legacy, two direct calls to predict.lm, lives on in predict.glm in R,which is based on the workaround for S3 written by Venables & Ripley.)

Further, the user is entitled to use positional matching when calling the generic, and thearguments to a method called by UseMethod are those of the call to the generic. Thus

A method must have arguments in exactly the same order as the generic.

To see the scale of this problem, consider the generic function scale, defined as

scale <- function (x, center = TRUE, scale = TRUE)

UseMethod("scale")

Suppose an unthinking package writer created methods such as

scale.foo <- function(x, scale = FALSE, ...) { }

Then for x of class "foo" the calls

scale(x, , TRUE)

scale(x, scale = TRUE)

would do most likely do different things, to the justifiable consternation of the end user.

To add a further twist, which default is used when a user calls scale(x) in our example?What if

Chapter 7: Generic functions and methods 136

scale.bar <- function(x, center, scale = TRUE) NextMethod("scale")

and x has class c("bar", "foo")? It is the default specified in the method that is used, but thedefault specified in the generic may be the one the user sees. This leads to the recommendation:

If the generic specifies defaults, all methods should use the same defaults.

An easy way to follow these recommendations is to always keep generics simple, e.g.

scale <- function(x, ...) UseMethod("scale")

Only add parameters and defaults to the generic if they make sense in all possible methodsimplementing it.

7.1 Adding new generics

When creating a new generic function, bear in mind that its argument list will be the maximalset of arguments for methods, including those written elsewhere years later. So choosing a goodset of arguments may well be an important design issue, and there need to be good argumentsnot to include a ... argument.

If a ... argument is supplied, some thought should be given to its position in the argumentsequence. Arguments which follow ... must be named in calls to the function, and they mustbe named in full (partial matching is suppressed after ...). Formal arguments before ... canbe partially matched, and so may ‘swallow’ actual arguments intended for .... Although it iscommonplace to make the ... argument the last one, that is not always the right choice.

Sometimes package writers want to make generic a function in the base package, and requesta change in R. This may be justifiable, but making a function generic with the old definition asthe default method does have a small performance cost. It is never necessary, as a package cantake over a function in the base package and make it generic by something like

foo <- function(object, ...) UseMethod("foo")

foo.default <- function(object, ...) base::foo(object)

Earlier versions of this manual suggested assigning foo.default <- base::foo. This is not agood idea, as it captures the base function at the time of installation and it might be changedas R is patched or updated.

The same idea can be applied for functions in other packages with namespaces.

Chapter 8: Linking GUIs and other front-ends to R 137

8 Linking GUIs and other front-ends to R

There are a number of ways to build front-ends to R: we take this to mean a GUI or otherapplication that has the ability to submit commands to R and perhaps to receive results back (notnecessarily in a text format). There are other routes besides those described here, for examplethe package Rserve (from CRAN, see also http://www.rforge.net/Rserve/) and connectionsto Java in ‘JRI’ (part of the rJava package on CRAN) and the Omegahat/Bioconductor package‘SJava’.

Note that the APIs described in this chapter are only intended to be used in an alternativefront-end: they are not part of the API made available for R packages and can be dangerousto use in a conventional package (although packages may contain alternative front-ends). Con-versely some of the functions from the API (such as R_alloc) should not be used in front-ends.

8.1 Embedding R under Unix-alikes

R can be built as a shared library1 if configured with --enable-R-shlib. This shared librarycan be used to run R from alternative front-end programs. We will assume this has been donefor the rest of this section. Also, it can be built as a static library if configured with --enable-

R-static-lib, and that can be used in a very similar way (at least on Linux: on other platformsone needs to ensure that all the symbols exported by libR.a and linked into the front-end).

The command-line R front-end, R_HOME/bin/exec/R, is one such example, and the formerGNOME (see package gnomeGUI on CRAN’s ‘Archive’ area) and OS X consoles are others. Thesource for R_HOME/bin/exec/R is in file src/main/Rmain.c and is very simple

int Rf_initialize_R(int ac, char **av); /* in ../unix/system.c */

void Rf_mainloop(); /* in main.c */

extern int R_running_as_main_program; /* in ../unix/system.c */

int main(int ac, char **av)

{

R_running_as_main_program = 1;

Rf_initialize_R(ac, av);

Rf_mainloop(); /* does not return */

return 0;

}

indeed, misleadingly simple. Remember that R_HOME/bin/exec/R is run from a shell scriptR_HOME/bin/R which sets up the environment for the executable, and this is used for

• Setting R_HOME and checking it is valid, as well as the path R_SHARE_DIR and R_DOC_DIR

to the installed share and doc directory trees. Also setting R_ARCH if needed.

• Setting LD_LIBRARY_PATH to include the directories used in linking R. This is recorded asthe default setting of R_LD_LIBRARY_PATH in the shell script R_HOME/etcR_ARCH/ldpaths.

• Processing some of the arguments, for example to run R under a debugger and to launchalternative front-ends to provide GUIs.

The first two of these can be achieved for your front-end by running it via R CMD. So, for example

R CMD /usr/local/lib/R/bin/exec/R

R CMD exec/R

will both work in a standard R installation. (R CMD looks first for executables in R_HOME/bin.These command-lines need modification if a sub-architecture is in use.) If you do not want to

1 In the parlance of OS X this is a dynamic library, and is the normal way to build R on that platform.

Chapter 8: Linking GUIs and other front-ends to R 138

run your front-end in this way, you need to ensure that R_HOME is set and LD_LIBRARY_PATH issuitable. (The latter might well be, but modern Unix/Linux systems do not normally include/usr/local/lib (/usr/local/lib64 on some architectures), and R does look there for systemcomponents.)

The other senses in which this example is too simple are that all the internal defaults are usedand that control is handed over to the R main loop. There are a number of small examples2 in thetests/Embedding directory. These make use of Rf_initEmbeddedR in src/main/Rembedded.c,and essentially use

#include <Rembedded.h>

int main(int ac, char **av)

{

/* do some setup */

Rf_initEmbeddedR(argc, argv);

/* do some more setup */

/* submit some code to R, which is done interactively via

run_Rmainloop();

A possible substitute for a pseudo-console is

R_ReplDLLinit();

while(R_ReplDLLdo1() > 0) {

/* add user actions here if desired */

}

*/

Rf_endEmbeddedR(0);

/* final tidying up after R is shutdown */

return 0;

}

If you do not want to pass R arguments, you can fake an argv array, for example by

char *argv[]= {"REmbeddedPostgres", "--silent"};

Rf_initEmbeddedR(sizeof(argv)/sizeof(argv[0]), argv);

However, to make a GUI we usually do want to run run_Rmainloop after setting up variousparts of R to talk to our GUI, and arranging for our GUI callbacks to be called during the Rmainloop.

One issue to watch is that on some platforms Rf_initEmbeddedR and Rf_endEmbeddedR

change the settings of the FPU (e.g. to allow errors to be trapped and to make use of extendedprecision registers).

The standard code sets up a session temporary directory in the usual way, unless R_TempDir

is set to a non-NULL value before Rf_initEmbeddedR is called. In that case the value is assumedto contain an existing writable directory (no check is done), and it is not cleaned up when R isshut down.

Rf_initEmbeddedR sets R to be in interactive mode: you can set R_Interactive (defined inRinterface.h) subsequently to change this.

Note that R expects to be run with the locale category ‘LC_NUMERIC’ set to its default valueof C, and so should not be embedded into an application which changes that.

2 but these are not part of the automated test procedures and so little tested.

Chapter 8: Linking GUIs and other front-ends to R 139

8.1.1 Compiling against the R library

Suitable flags to compile and link against the R (shared or static) library can be found by

R CMD config --cppflags

R CMD config --ldflags

If R is installed, pkg-config is available and sub-architectures have not been used, alterna-tives for a shared R library are

pkg-config --cflags libR

pkg-config --libs libR

and for a static R library

pkg-config --cflags libR

pkg-config --libs --static libR

However, these may not suffice and a more comprehensive way is to set up a Makefile tocompile the front-end. Suppose file myfe.c is to be compiled to myfe. A suitable Makefile

might be

include ${R_HOME}/etc${R_ARCH}/Makeconf

all: myfe

## The following is not needed, but avoids PIC flags.

myfe.o: myfe.c

$(CC) $(ALL_CPPFLAGS) $(CFLAGS) -c myfe.c -o $@

## replace $(LIBR) $(LIBS) by $(STATIC_LIBR) if R was build with a static libR

myfe: myfe.o

$(MAIN_LINK) -o $@ myfe.o $(LIBR) $(LIBS)

invoked as

R CMD make

R CMD myfe

Additional flags which $(MAIN_LINK) includes are, amongst others, those to select OpenMPand --export-dynamic for the GNU linker on some platforms. In principle $(LIBS) is notneeded when using a shared R library as libR is linked against those libraries, but some platformsneed the executable also linked against them.

8.1.2 Setting R callbacks

For Unix-alikes there is a public header file Rinterface.h that makes it possible to change thestandard callbacks used by R in a documented way. This defines pointers (if R_INTERFACE_PTRSis defined)

extern void (*ptr_R_Suicide)(const char *);

extern void (*ptr_R_ShowMessage)(const char *);

extern int (*ptr_R_ReadConsole)(const char *, unsigned char *, int, int);

extern void (*ptr_R_WriteConsole)(const char *, int);

extern void (*ptr_R_WriteConsoleEx)(const char *, int, int);

extern void (*ptr_R_ResetConsole)();

extern void (*ptr_R_FlushConsole)();

extern void (*ptr_R_ClearerrConsole)();

extern void (*ptr_R_Busy)(int);

extern void (*ptr_R_CleanUp)(SA_TYPE, int, int);

extern int (*ptr_R_ShowFiles)(int, const char **, const char **,

const char *, Rboolean, const char *);

extern int (*ptr_R_ChooseFile)(int, char *, int);

Chapter 8: Linking GUIs and other front-ends to R 140

extern int (*ptr_R_EditFile)(const char *);

extern void (*ptr_R_loadhistory)(SEXP, SEXP, SEXP, SEXP);

extern void (*ptr_R_savehistory)(SEXP, SEXP, SEXP, SEXP);

extern void (*ptr_R_addhistory)(SEXP, SEXP, SEXP, SEXP);

// added in R 3.0.0

extern int (*ptr_R_EditFiles)(int, const char **, const char **, const char *);

extern SEXP (*ptr_do_selectlist)(SEXP, SEXP, SEXP, SEXP);

extern SEXP (*ptr_do_dataentry)(SEXP, SEXP, SEXP, SEXP);

extern SEXP (*ptr_do_dataviewer)(SEXP, SEXP, SEXP, SEXP);

extern void (*ptr_R_ProcessEvents)();

which allow standard R callbacks to be redirected to your GUI. What these do is generallydocumented in the file src/unix/system.txt.

[Function]void R_ShowMessage (char *message)This should display the message, which may have multiple lines: it should be brought to theuser’s attention immediately.

[Function]void R_Busy (int which)This function invokes actions (such as change of cursor) when R embarks on an extendedcomputation (which=1) and when such a state terminates (which=0).

[Function]int R_ReadConsole (const char *prompt, unsigned char *buf, int buflen,int hist)

[Function]void R_WriteConsole (const char *buf, int buflen)[Function]void R_WriteConsoleEx (const char *buf, int buflen, int otype)[Function]void R_ResetConsole ()[Function]void R_FlushConsole ()[Function]void R_ClearErrConsole ()

These functions interact with a console.

R_ReadConsole prints the given prompt at the console and then does a fgets(3)–like oper-ation, transferring up to buflen characters into the buffer buf. The last two bytes should beset to ‘"\n\0"’ to preserve sanity. If hist is non-zero, then the line should be added to anycommand history which is being maintained. The return value is 0 is no input is availableand >0 otherwise.

R_WriteConsoleEx writes the given buffer to the console, otype specifies the output type(regular output or warning/error). Call to R_WriteConsole(buf, buflen) is equivalent toR_WriteConsoleEx(buf, buflen, 0). To ensure backward compatibility of the callbacks,ptr_R_WriteConsoleEx is used only if ptr_R_WriteConsole is set to NULL. To ensure thatstdout() and stderr() connections point to the console, set the corresponding files to NULL

via

R_Outputfile = NULL;

R_Consolefile = NULL;

R_ResetConsole is called when the system is reset after an error. R_FlushConsole is calledto flush any pending output to the system console. R_ClearerrConsole clears any errorsassociated with reading from the console.

[Function]int R_ShowFiles (int nfile, const char **file, const char **headers,const char *wtitle, Rboolean del, const char *pager)

This function is used to display the contents of files.

[Function]int R_ChooseFile (int new, char *buf, int len)Choose a file and return its name in buf of length len. Return value is 0 for success, > 0otherwise.

Chapter 8: Linking GUIs and other front-ends to R 141

[Function]int R_EditFile (const char *buf)Send a file to an editor window.

[Function]int R_EditFiles (int nfile, const char **file, const char **title, constchar *editor)

Send nfile files to an editor, with titles possibly to be used for the editor window(s).

[Function]SEXP R_loadhistory (SEXP, SEXP, SEXP, SEXP);[Function]SEXP R_savehistory (SEXP, SEXP, SEXP, SEXP);[Function]SEXP R_addhistory (SEXP, SEXP, SEXP, SEXP);

.Internal functions for loadhistory, savehistory and timestamp.

If the console has no history mechanism these can be as simple as

SEXP R_loadhistory (SEXP call, SEXP op, SEXP args, SEXP env)

{

errorcall(call, "loadhistory is not implemented");

return R_NilValue;

}

SEXP R_savehistory (SEXP call, SEXP op , SEXP args, SEXP env)

{

errorcall(call, "savehistory is not implemented");

return R_NilValue;

}

SEXP R_addhistory (SEXP call, SEXP op , SEXP args, SEXP env)

{

return R_NilValue;

}

The R_addhistory function should return silently if no history mechanism is present, as auser may be calling timestamp purely to write the time stamp to the console.

[Function]void R_Suicide (const char *message)This should abort R as rapidly as possible, displaying the message. A possible implementationis

void R_Suicide (const char *message)

{

char pp[1024];

snprintf(pp, 1024, "Fatal error: %s\n", s);

R_ShowMessage(pp);

R_CleanUp(SA_SUICIDE, 2, 0);

}

[Function]void R_CleanUp (SA TYPE saveact, int status, int RunLast)This function invokes any actions which occur at system termination. It needs to be quitecomplex:

#include <Rinterface.h>

#include <Rembedded.h> /* for Rf_KillAllDevices */

void R_CleanUp (SA_TYPE saveact, int status, int RunLast)

{

if(saveact == SA_DEFAULT) saveact = SaveAction;

if(saveact == SA_SAVEASK) {

/* ask what to do and set saveact */

}

Chapter 8: Linking GUIs and other front-ends to R 142

switch (saveact) {

case SA_SAVE:

if(runLast) R_dot_Last();

if(R_DirtyImage) R_SaveGlobalEnv();

/* save the console history in R_HistoryFile */

break;

case SA_NOSAVE:

if(runLast) R_dot_Last();

break;

case SA_SUICIDE:

default:

break;

}

R_RunExitFinalizers();

/* clean up after the editor e.g. CleanEd() */

R_CleanTempDir();

/* close all the graphics devices */

if(saveact != SA_SUICIDE) Rf_KillAllDevices();

fpu_setup(FALSE);

exit(status);

}

These callbacks should never be changed in a running R session (and hence cannot be calledfrom an extension package).

[Function]SEXP R_dataentry (SEXP, SEXP, SEXP, SEXP);[Function]SEXP R_dataviewer (SEXP, SEXP, SEXP, SEXP);[Function]SEXP R_selectlist (SEXP, SEXP, SEXP, SEXP);

.External functions for dataentry (and edit on matrices and data frames), View andselect.list. These can be changed if they are not currently in use.

8.1.3 Registering symbols

An application embedding R needs a different way of registering symbols because it is not adynamic library loaded by R as would be the case with a package. Therefore R reserves aspecial DllInfo entry for the embedding application such that it can register symbols to beused with .C, .Call etc. This entry can be obtained by calling getEmbeddingDllInfo, so atypical use is

DllInfo *info = R_getEmbeddingDllInfo();

R_registerRoutines(info, cMethods, callMethods, NULL, NULL);

The native routines defined by cMethods and callMethods should be present in the embed-ding application. See Section 5.4 [Registering native routines], page 88 for details on registeringsymbols in general.

8.1.4 Meshing event loops

One of the most difficult issues in interfacing R to a front-end is the handling of event loops, atleast if a single thread is used. R uses events and timers for

• Running X11 windows such as the graphics device and data editor, and interacting withthem (e.g., using locator()).

Chapter 8: Linking GUIs and other front-ends to R 143

• Supporting Tcl/Tk events for the tcltk package (for at least the X11 version of Tk).

• Preparing input.

• Timing operations, for example for profiling R code and Sys.sleep().

• Interrupts, where permitted.

Specifically, the Unix-alike command-line version of R runs separate event loops for

• Preparing input at the console command-line, in file src/unix/sys-unix.c.

• Waiting for a response from a socket in the internal functions underlying FTP and HTTPtransfers in download.file() and for direct socket access, in files src/modules/internet/nanoftp.c, src/modules/internet/nanohttp.c and src/modules/internet/Rsock.c

• Mouse and window events when displaying the X11-based dataentry window, in filesrc/modules/X11/dataentry.c. This is regarded as modal, and no other events are ser-viced whilst it is active.

There is a protocol for adding event handlers to the first two types of event loops, usingtypes and functions declared in the header R_ext/eventloop.h and described in comments infile src/unix/sys-std.c. It is possible to add (or remove) an input handler for events on aparticular file descriptor, or to set a polling interval (via R_wait_usec) and a function to becalled periodically via R_PolledEvents: the polling mechanism is used by the tcltk package.

It is not intended that these facilities are used by packages, but if they are needed exception-ally, the package should ensure that it cleans up and removes its handlers when its namespaceis unloaded.

An alternative front-end needs both to make provision for other R events whilst waiting forinput, and to ensure that it is not frozen out during events of the second type. This is nothandled very well in the existing examples. The GNOME front-end ran a private handler forpolled events by setting

extern int (*R_timeout_handler)();

extern long R_timeout_val;

if (R_timeout_handler && R_timeout_val)

gtk_timeout_add(R_timeout_val, R_timeout_handler, NULL);

gtk_main ();

whilst it is waiting for console input. This obviously handles events for Gtk windows (such as thegraphics device in the gtkDevice package), but not X11 events (such as the X11() device) or forother event handlers that might have been registered with R. It does not attempt to keep itselfalive whilst R is waiting on sockets. The ability to add a polled handler as R_timeout_handleris used by the tcltk package.

8.1.5 Threading issues

Embedded R is designed to be run in the main thread, and all the testing is done in that context.There is a potential issue with the stack-checking mechanism where threads are involved. Thisuses two variables declared in Rinterface.h (if CSTACK_DEFNS is defined) as

extern uintptr_t R_CStackLimit; /* C stack limit */

extern uintptr_t R_CStackStart; /* Initial stack address */

Note that uintptr_t is a C99 type for which a substitute is defined in R, so your code needs todefine HAVE_UINTPTR_T appropriately.

These will be set3 when Rf_initialize_R is called, to values appropriate to the main thread.Stack-checking can be disabled by setting R_CStackLimit = (uintptr_t)-1, but it is better to

3 at least on platforms where the values are available, that is having getrlimit and on Linux or having sysctl

supporting KERN_USRSTACK, including FreeBSD and OS X.

Chapter 8: Linking GUIs and other front-ends to R 144

if possible set appropriate values. (What these are and how to determine them are OS-specific,and the stack size limit may differ for secondary threads. If you have a choice of stack size, atleast 10Mb is recommended.)

You may also want to consider how signals are handled: R sets signal handlers for sev-eral signals, including SIGINT, SIGSEGV, SIGPIPE, SIGUSR1 and SIGUSR2, but these can all besuppressed by setting the variable R_SignalHandlers (declared in Rinterface.h) to 0.

Note that these variables must not be changed by an R package: a package should not callingR internals which makes use of the stack-checking mechanism on a secondary thread.

8.2 Embedding R under Windows

All Windows interfaces to R call entry points in the DLL R.dll, directly or indirectly. Simplerapplications may find it easier to use the indirect route via (D)COM.

8.2.1 Using (D)COM

(D)COM is a standard Windows mechanism used for communication between Windows appli-cations. One application (here R) is run as COM server which offers services to clients, herethe front-end calling application. The services are described in a ‘Type Library’ and are (moreor less) language-independent, so the calling application can be written in C or C++ or VisualBasic or Perl or Python and so on. The ‘D’ in (D)COM refers to ‘distributed’, as the client andserver can be running on different machines.

The basic R distribution is not a (D)COM server, but two addons are currently availablethat interface directly with R and provide a (D)COM server:

• There is a (D)COM server called StatConnector written by Thomas Baier available viahttp://sunsite.univie.ac.at/rcom/, which works with R packages to support transferof data to and from R and remote execution of R commands, as well as embedding of an Rgraphics window.

Recent versions have usage restrictions.

• Another (D)COM server, RDCOMServer, is available from http://www.omegahat.org/.Its philosophy is discussed in http://www.omegahat.org/RDCOMServer/Docs/Paradigm.

html and is very different from the purpose of this section.

8.2.2 Calling R.dll directly

The R DLL is mainly written in C and has _cdecl entry points. Calling it directly will be trickyexcept from C code (or C++ with a little care).

There is a version of the Unix-alike interface calling

int Rf_initEmbeddedR(int ac, char **av);

void Rf_endEmbeddedR(int fatal);

which is an entry point in R.dll. Examples of its use (and a suitable Makefile.win) can be foundin the tests/Embedding directory of the sources. You may need to ensure that R_HOME/bin isin your PATH so the R DLLs are found.

Examples of calling R.dll directly are provided in the directory src/gnuwin32/front-ends,including a simple command-line front end rtest.c whose code is

#define Win32

#include <windows.h>

#include <stdio.h>

#include <Rversion.h>

#define LibExtern __declspec(dllimport) extern

#include <Rembedded.h>

#include <R_ext/RStartup.h>

/* for askok and askyesnocancel */

Chapter 8: Linking GUIs and other front-ends to R 145

#include <graphapp.h>

/* for signal-handling code */

#include <psignal.h>

/* simple input, simple output */

/* This version blocks all events: a real one needs to call ProcessEvents

frequently. See rterm.c and ../system.c for one approach using

a separate thread for input.

*/

int myReadConsole(const char *prompt, char *buf, int len, int addtohistory)

{

fputs(prompt, stdout);

fflush(stdout);

if(fgets(buf, len, stdin)) return 1; else return 0;

}

void myWriteConsole(const char *buf, int len)

{

printf("%s", buf);

}

void myCallBack(void)

{

/* called during i/o, eval, graphics in ProcessEvents */

}

void myBusy(int which)

{

/* set a busy cursor ... if which = 1, unset if which = 0 */

}

static void my_onintr(int sig) { UserBreak = 1; }

int main (int argc, char **argv)

{

structRstart rp;

Rstart Rp = &rp;

char Rversion[25], *RHome;

sprintf(Rversion, "%s.%s", R_MAJOR, R_MINOR);

if(strcmp(getDLLVersion(), Rversion) != 0) {

fprintf(stderr, "Error: R.DLL version does not match\n");

exit(1);

}

R_setStartTime();

R_DefParams(Rp);

if((RHome = get_R_HOME()) == NULL) {

fprintf(stderr, "R_HOME must be set in the environment or Registry\n");

exit(1);

}

Rp->rhome = RHome;

Rp->home = getRUser();

Rp->CharacterMode = LinkDLL;

Rp->ReadConsole = myReadConsole;

Rp->WriteConsole = myWriteConsole;

Rp->CallBack = myCallBack;

Rp->ShowMessage = askok;

Rp->YesNoCancel = askyesnocancel;

Rp->Busy = myBusy;

Rp->R_Quiet = TRUE; /* Default is FALSE */

Rp->R_Interactive = FALSE; /* Default is TRUE */

Chapter 8: Linking GUIs and other front-ends to R 146

Rp->RestoreAction = SA_RESTORE;

Rp->SaveAction = SA_NOSAVE;

R_SetParams(Rp);

R_set_command_line_arguments(argc, argv);

FlushConsoleInputBuffer(GetStdHandle(STD_INPUT_HANDLE));

signal(SIGBREAK, my_onintr);

GA_initapp(0, 0);

readconsolecfg();

setup_Rmainloop();

#ifdef SIMPLE_CASE

run_Rmainloop();

#else

R_ReplDLLinit();

while(R_ReplDLLdo1() > 0) {

/* add user actions here if desired */

}

/* only get here on EOF (not q()) */

#endif

Rf_endEmbeddedR(0);

return 0;

}

The ideas are

• Check that the front-end and the linked R.dll match – other front-ends may allow a loosermatch.

• Find and set the R home directory and the user’s home directory. The formermay be available from the Windows Registry: it will be in HKEY_LOCAL_

MACHINE\Software\R-core\R\InstallPath from an administrative install andHKEY_CURRENT_USER\Software\R-core\R\InstallPath otherwise, if selected duringinstallation (as it is by default).

• Define startup conditions and callbacks via the Rstart structure. R_DefParams sets thedefaults, and R_SetParams sets updated values.

• Record the command-line arguments used by R_set_command_line_arguments for use bythe R function commandArgs().

• Set up the signal handler and the basic user interface.

• Run the main R loop, possibly with our actions intermeshed.

• Arrange to clean up.

An underlying theme is the need to keep the GUI ‘alive’, and this has not been done inthis example. The R callback R_ProcessEvents needs to be called frequently to ensure thatWindows events in R windows are handled expeditiously. Conversely, R needs to allow the GUIcode (which is running in the same process) to update itself as needed – two ways are providedto allow this:

• R_ProcessEvents calls the callback registered by Rp->callback. A version of this is usedto run package Tcl/Tk for tcltk under Windows, for the code is

void R_ProcessEvents(void)

{

while (peekevent()) doevent(); /* Windows events for GraphApp */

if (UserBreak) { UserBreak = FALSE; onintr(); }

R_CallBackHook();

if(R_tcldo) R_tcldo();

}

• The mainloop can be split up to allow the calling application to take some action after eachline of input has been dealt with: see the alternative code below #ifdef SIMPLE_CASE.

Chapter 8: Linking GUIs and other front-ends to R 147

It may be that no R GraphApp windows need to be considered, although these includepagers, the windows() graphics device, the R data and script editors and various popups suchas choose.file() and select.list(). It would be possible to replace all of these, but it seemseasier to allow GraphApp to handle most of them.

It is possible to run R in a GUI in a single thread (as RGui.exe shows) but it will normallybe easier4 to use multiple threads.

Note that R’s own front ends use a stack size of 10Mb, whereas MinGW executables defaultto 2Mb, and Visual C++ ones to 1Mb. The latter stack sizes are too small for a number of Rapplications, so general-purpose front-ends should use a larger stack size.

8.2.3 Finding R HOME

Both applications which embed R and those which use a system call to invoke R (asRscript.exe, Rterm.exe or R.exe) need to be able to find the R bin directory. The sim-plest way to do so is the ask the user to set an environment variable R_HOME and use that, butnaive users may be flummoxed as to how to do so or what value to use.

The R for Windows installers have for a long time allowed the value of R_HOME to be recordedin the Windows Registry: this is optional but selected by default. Where it is recorded haschanged over the years to allow for multiple versions of R to be installed at once, and to allow32- and 64-bit versions of R to be installed on the same machine.

The basic Registry location is Software\R-core\R. For an administrative install this isunder HKEY_LOCAL_MACHINE and on a 64-bit OS HKEY_LOCAL_MACHINE\Software\R-core\R isby default redirected for a 32-bit application, so a 32-bit application will see the information forthe last 32-bit install, and a 64-bit application that for the last 64-bit install. For a personalinstall, the information is under HKEY_CURRENT_USER\Software\R-core\R which is seen by both32-bit and 64-bit applications and so records the last install of either architecture. To circumventthis, there are locations Software\R-core\R32 and Software\R-core\R64 which always referto one architecture.

When R is installed and recording is not disabled then two string values are written at thatlocation for keys InstallPath and Current Version, and these keys are removed when R isuninstalled. To allow information about other installed versions to be retained, there is alsoa key named something like 3.0.0 or 3.0.0 patched or 3.1.0 Pre-release with a value forInstallPath.

So a comprehensive algorithm to search for R_HOME is something like

• Decide which of personal or administrative installs should have precedence. There are argu-ments both ways: we find that with roaming profiles that HKEY_CURRENT_USER\Softwareoften gets reverted to an earlier version. Do the following for one or both of HKEY_CURRENT_USER and HKEY_LOCAL_MACHINE.

• If the desired architecture is known, look in Software\R-core\R32 orSoftware\R-core\R64, and if that does not exist or the architecture is immate-rial, in Software\R-core\R.

• If key InstallPath exists then this is R_HOME (recorded using backslashes). If it doesnot, look for version-specific keys like 2.11.0 alpha, pick the latest (which is of itself acomplicated algorithm as 2.11.0 patched > 2.11.0 > 2.11.0 alpha > 2.8.1) and use itsvalue for InstallPath.

Prior to R 2.12.0 R.dll and the various front-end executables were in R_HOME\bin, but theyare now in R_HOME\bin\i386 or R_HOME\bin\x64. So you may need to arrange to look first inthe architecture-specific subdirectory and then in R_HOME\bin.

4 An attempt to use only threads in the late 1990s failed to work correctly under Windows 95, the predominantversion of Windows at that time.

Function and variable index 148

Function and variable index

.

.C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

.Call . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97, 106

.External . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97, 106

.Fortran . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

.Last.lib . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

.onAttach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

.onDetach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

.onLoad . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

.onUnload . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

.Random.seed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121

\\acronym . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59\alias . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51\arguments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53\author . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54\bold . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57\cite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59\code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58\command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59\concept . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62\cr . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57\deqn . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60\describe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59\description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52\details . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53\dfn . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59\dontrun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54\dontshow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54\dots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61\dQuote . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57\email . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58\emph . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57\enc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62\enumerate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59\env . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59\eqn . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60\examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54\figure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61\file . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58\format . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56\href . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58\if . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63\ifelse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63\itemize . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59\kbd . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58\keyword . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55\ldots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61\link . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60\method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52\name . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51\newcommand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64\note . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54\option . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59\out . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63\pkg . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58\preformatted . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58\R . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61\RdOpts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

\references . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54\renewcommand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64\S3method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53\samp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58\section . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57\seealso . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54\Sexpr . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63\source . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56\sQuote . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57\strong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57\tabular . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59\title . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52\url . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58\usage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52\value . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53\var . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58\verb . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

AallocVector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99AUTHORS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

Bbessel_i . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125bessel_j . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125bessel_k . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125bessel_y . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125beta . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125BLAS_LIBS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18browser . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

CCalloc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120CAR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107CDR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107cgmin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128choose . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125CITATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13, 48COPYRIGHTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5, 13cPsort . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130

Ddebug . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76debugger . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75defineVar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104digamma . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125dump.frames . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75duplicate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105dyn.load . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87dyn.unload . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87

Eexp_rand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121expm1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126export . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35exportClasses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

Function and variable index 149

exportClassPattern . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39exportMethods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39exportPattern . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35, 39

FFALSE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127findInterval . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130findVar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104FLIBS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18fmax2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126fmin2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126fprec . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126Free . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120fround . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126fsign . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126ftrunc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126

Ggammafn . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125gctorture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78getAttrib . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101getCharCE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118GetRNGstate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121

Iimax2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126imin2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126import . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35importClassesFrom . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40importFrom . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35importMethodsFrom . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40install . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102iPsort . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130ISNA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108, 122ISNAN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108, 122

LLAPACK_LIBS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19lbeta . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125lbfgsb . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128lchoose . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125lgamma1p . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126lgammafn . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125library.dynam . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11, 87log1p . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126log1pexp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126log1pmx . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126logspace_add . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126logspace_sub . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126

MM_E . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127M_PI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127mkChar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103mkCharCE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118mkCharLen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103mkCharLenCE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118

NNA_REAL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122NEWS.Rd . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13nmmin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128norm_rand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121

OOBJECTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19, 92

Ppentagamma . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125PKG_CFLAGS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92PKG_CPPFLAGS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92PKG_CXXFLAGS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92PKG_FCFLAGS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92PKG_FFLAGS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92PKG_LIBS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92PKG_OBJCFLAGS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92prompt . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51PROTECT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98PROTECT_WITH_INDEX . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99psigamma . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125PutRNGstate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121

Qqsort3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130qsort4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130

RR CMD build . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29R CMD check . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26R CMD config . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16R CMD Rd2pdf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65R CMD Rdconv . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65R CMD SHLIB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92R CMD Stangle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65R CMD Sweave . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65R_addhistory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141R_alloc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119R_Busy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140R_ChooseFile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140R_CleanUp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141R_ClearErrConsole . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140R_csort . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129R_dataentry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142R_dataviewer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142R_EditFile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141R_EditFiles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141R_ExpandFileName . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131R_FINITE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122R_FlushConsole . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140R_GetCCallable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91R_GetCurrentSrcref . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115R_GetSrcFilename . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115R_INLINE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132R_IsNaN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122R_isort . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129R_LIBRARY_DIR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17R_loadhistory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141R_max_col . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130

Function and variable index 150

R_NegInf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122R_orderVector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129R_PACKAGE_DIR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17R_PACKAGE_NAME . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17R_ParseVector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115R_PosInf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122R_pow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126R_pow_di . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126R_qsort . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130R_qsort_I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130R_qsort_int . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130R_qsort_int_I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130R_ReadConsole . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140R_RegisterCCallable . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91R_registerRoutines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89R_ResetConsole . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140R_rsort . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129R_savehistory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141R_selectlist . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142R_ShowFiles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140R_ShowMessage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140R_Srcref . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115R_Suicide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141R_tmpnam . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130R_tmpnam2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130R_Version . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132R_WriteConsole . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140R_WriteConsoleEx . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140Rdqagi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128Rdqags . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128Realloc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120recover . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76reEnc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118REprintf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122REPROTECT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99REvprintf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122revsort . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130Riconv . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131Riconv_close . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131Riconv_open . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131Rprintf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122Rprof . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67, 69Rprofmem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70rPsort . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130rsort_with_index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129

Rvprintf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122

SS_alloc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119S_realloc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119S3method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35SAFE_FFLAGS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19samin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128seed_in . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121seed_out . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121setAttrib . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101setVar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104sign . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126summaryRprof . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85system.time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85system2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

Ttetragamma . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125trace . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77traceback . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74tracemem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70translateChar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118translateCharUTF8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118trigamma . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125TRUE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127

Uundebug . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77unif_rand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121UNPROTECT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98UNPROTECT_PTR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99untracemem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70useDynLib . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

Vvmaxget . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119vmaxset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119vmmin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128

Concept index 151

Concept index

.

.install extras file . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

.Rbuildignore file . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

.Rinstignore file . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

\\linkS4class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

AAllocating storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99Attributes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100

BBessel functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125Beta function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125Building binary packages . . . . . . . . . . . . . . . . . . . . . . . . . 30Building source packages . . . . . . . . . . . . . . . . . . . . . . . . . 29

CC++ code, interfacing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93Calling C from FORTRAN and vice versa . . . . . . . 123Checking packages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26citation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13, 48Classes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102cleanup file . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3conditionals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63configure file . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3Copying objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105CRAN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3Creating packages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2Creating shared objects . . . . . . . . . . . . . . . . . . . . . . . . . . 92Cross-references in documentation . . . . . . . . . . . . . . . . 60cumulative hazard . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124

DDebugging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81DESCRIPTION file . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4Details of R types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99Distribution functions from C . . . . . . . . . . . . . . . . . . . 124Documentation, writing . . . . . . . . . . . . . . . . . . . . . . . . . . 50Dynamic loading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87dynamic pages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

EEditing Rd files . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66encoding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64Error handling from C . . . . . . . . . . . . . . . . . . . . . . . . . . 121Error handling from FORTRAN . . . . . . . . . . . . . . . . . 121Evaluating R expressions from C . . . . . . . . . . . . . . . . 108external pointer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116

FFigures in documentation . . . . . . . . . . . . . . . . . . . . . . . . 61

finalizer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116Finding variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103

GGamma function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125Garbage collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98Generic functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135

Hhandling character data . . . . . . . . . . . . . . . . . . . . . . . . . 103Handling lists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102Handling R objects in C . . . . . . . . . . . . . . . . . . . . . . . . . . 97

IIEEE special values . . . . . . . . . . . . . . . . . . . . . . . . 108, 122INDEX file . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10Indices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62Inspecting R objects when debugging . . . . . . . . . . . . . 82integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128Interfaces to compiled code . . . . . . . . . . . . . . . . . . 85, 106Interfacing C++ code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93Interrupts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131

LLICENCE file . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8LICENSE file . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8Lists and tables in documentation . . . . . . . . . . . . . . . . 59

MMarking text in documentation . . . . . . . . . . . . . . . . . . . 57Mathematics in documentation . . . . . . . . . . . . . . . . . . . 60Memory allocation from C . . . . . . . . . . . . . . . . . . . . . . 119Memory use . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69Method functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135Missing values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108, 122

Nnamespaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34news . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13Numerical analysis subroutines from C . . . . . . . . . . 123Numerical derivatives . . . . . . . . . . . . . . . . . . . . . . . . . . . 111

OOpenMP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21, 132Operating system access . . . . . . . . . . . . . . . . . . . . . . . . . . 85optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127

PPackage builder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29Package structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3Package subdirectories . . . . . . . . . . . . . . . . . . . . . . . . . . . 11Packages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Concept index 152

Parsing R code from C . . . . . . . . . . . . . . . . . . . . . . . . . . 114Platform-specific documentation . . . . . . . . . . . . . . . . . . 62Printing from C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122Printing from FORTRAN . . . . . . . . . . . . . . . . . . . . . . . 122Processing Rd format . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65Profiling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67, 69, 70

RRandom numbers in C . . . . . . . . . . . . . . . . . . . . . 121, 124Random numbers in FORTRAN . . . . . . . . . . . . . . . . 123Registering native routines . . . . . . . . . . . . . . . . . . . . . . . 88

SSetting variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103Sort functions from C . . . . . . . . . . . . . . . . . . . . . . . . . . . 129Sweave . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

Ttarballs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29Tidying R code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

Uuser-defined macros . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

VVersion information from C . . . . . . . . . . . . . . . . . . . . . 132vignettes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31Visibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132

Wweak reference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116

ZZero-finding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110